Consulting Engagement with the Chilean Government

Representatives of Chilean government (the Ministry of Economic Development and Tourism of Chile) hired a consulting company, trying to improve the tourism industry in the country. Government officials provided some existing data (messy as always!) and expect meaninful insights and recommendations.

Project objective:

Based on the data associated to the 15 regions of Chile under the 5 dimensions considered, you are expected to deliver a report that will include the three sections outlined below:

Perform a Principal Component Analysis (PCA) for the dataset provided.

Based on the values obtained for each region under each of the ten dimensions, come up with an overall tourism competitiveness ranking for all regions (as presented in the example available in the previous module).

In [7]:
# Import useful libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
pass
In [8]:
# Set additional parameters for charts and tables

# Remove column display limits
pd.set_option('display.max_columns', None)
# pd.set_option('display.height', None)
pd.set_option('display.max_rows', None)
# pd.set_option('display.width', None)
plt.rcParams['figure.figsize'] = [15, 10]
sns.set_style("white")

1. Data Importing and Data Cleaning

In [9]:
# Read data in csv format, using encoding, and read the third row as column names
chile_data_1 = pd.read_csv('Tourism Chile D1 - D5.csv', encoding = 'ISO-8859-1', header = 3)
chile_data_1.drop(chile_data_1.iloc[:, 0:2], inplace = True, axis = 1)
In [10]:
# Print
chile_data_1.head()
Out[10]:
VARIABLE CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
0 Arica y Parinacota 31.0 2 0 30 28 4 22.1 2 105 12 2 25.4 1 0 0 0 4 32 4 3 9 32 7 59 - 21.9 0.46 58.00 1 0.00 5 - 13 13 4 6 6 2 0 8 2 0 0 2 - 0 1 42.556 0.88 $ 293,648 94.1 83.8 11.1 53.0 2 193 11 20.038 356 23.69 18.74 6,544 33.01 37.0 2.3 5 3 0.0 0 97,454 34,186 1,151,575 5.2730 2.129 17.35 45,248 15,045 13 167,211 4.67 3
1 Tarapacá 0.0 5 1 13 73 5 20.8 2 178 12 1 12.6 1 0 0 0 0 34 10 1 6 34 12 0 0.2 9.1 0.03 76.03 1 0.00 16 - 2 6 1 1 7 5 0 5 6 4 2 3 5 0 0 68.563 1.45 $ 381,466 91.7 66.7 10.7 42.0 5 255 19 22.180 380 23.03 22.17 11,108 41.43 42.8 2.1 5 10 0.0 11 235,365 40,919 19,560 4.1850 4.021 15.90 81,182 17,161 0 434,727 184.10 1
2 Antofagasta 1.0 9 0 28 81 16 27.4 8 203 15 2 5.7 2 1 0 3 1 24 37 0 4 31 26 63 - 2.8 0.03 39.56 2 0.00 22 - 5 28 8 13 3 14 1 6 10 6 4 0 5 0 0 54.486 1.08 $ 475,866 91.7 66.7 10.6 40.0 7 529 47 20.446 184 24.55 22.76 19,920 35.02 44.6 1.8 2 10 17.0 15 413,922 84,195 22,898 2.0244 3.332 52.25 112,607 315,888 0 115,100 23.00 5
3 Atacama 8.0 10 0 8 35 7 20.0 0 144 12 0 7.5 0 0 0 0 0 18 5 0 2 29 7 33 - 2.0 0.02 40.76 0 3.93 17 NaN 7 23 2 7 2 10 0 3 13 1 3 2 5 1 0 51.844 0.94 $ 379,971 89.1 73.2 10.3 47.0 1 108 16 18.479 578 19.43 18.49 9,674 29.25 36.0 1.9 1 3 0.0 15 168,508 14,222 2,416 0.0000 3.635 24.39 55,812 24,873 0 2,552 11.33 2
4 Coquimbo 23.0 7 0 2 52 7 18.3 4 80 22 2 1.7 0 1 0 7 1 69 2 1 0 29 27 97 0.8 0.4 0.35 25.45 1 0.00 37 1.50 4 16 7 0 1 21 0 18 0 9 1 0 5 2 1 56.498 0.92 $ 338,014 92.7 71.2 9.7 43.0 7 870 85 6.963 1074 15.50 15.63 24,346 12.00 35.2 2.8 4 9 383.9 750 205,850 25,803 2,786 0.0000 1.912 283.37 116,263 57,234 0 15,265 228.58 1
In [11]:
# Remove the last row with aggregated Total data
chile_data_1 = chile_data_1[:-1]
In [12]:
chile_data_1
Out[12]:
VARIABLE CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
0 Arica y Parinacota 31.0 2 0 30 28 4 22.1 2 105 12 2 25.4 1 0 0 0 4 32 4 3 9 32 7 59 - 21.9 0.46 58.00 1 0.00 5 - 13 13 4 6 6 2 0 8 2 0 0 2 - 0 1 42.556 0.88 $ 293,648 94.1 83.8 11.1 53.0 2 193 11 20.038 356 23.69 18.74 6,544 33.01 37.0 2.3 5 3 0.0 0 97,454 34,186 1,151,575 5.2730 2.129 17.35 45,248 15,045 13 167,211 4.67 3
1 Tarapacá 0.0 5 1 13 73 5 20.8 2 178 12 1 12.6 1 0 0 0 0 34 10 1 6 34 12 0 0.2 9.1 0.03 76.03 1 0.00 16 - 2 6 1 1 7 5 0 5 6 4 2 3 5 0 0 68.563 1.45 $ 381,466 91.7 66.7 10.7 42.0 5 255 19 22.180 380 23.03 22.17 11,108 41.43 42.8 2.1 5 10 0.0 11 235,365 40,919 19,560 4.1850 4.021 15.90 81,182 17,161 0 434,727 184.10 1
2 Antofagasta 1.0 9 0 28 81 16 27.4 8 203 15 2 5.7 2 1 0 3 1 24 37 0 4 31 26 63 - 2.8 0.03 39.56 2 0.00 22 - 5 28 8 13 3 14 1 6 10 6 4 0 5 0 0 54.486 1.08 $ 475,866 91.7 66.7 10.6 40.0 7 529 47 20.446 184 24.55 22.76 19,920 35.02 44.6 1.8 2 10 17.0 15 413,922 84,195 22,898 2.0244 3.332 52.25 112,607 315,888 0 115,100 23.00 5
3 Atacama 8.0 10 0 8 35 7 20.0 0 144 12 0 7.5 0 0 0 0 0 18 5 0 2 29 7 33 - 2.0 0.02 40.76 0 3.93 17 NaN 7 23 2 7 2 10 0 3 13 1 3 2 5 1 0 51.844 0.94 $ 379,971 89.1 73.2 10.3 47.0 1 108 16 18.479 578 19.43 18.49 9,674 29.25 36.0 1.9 1 3 0.0 15 168,508 14,222 2,416 0.0000 3.635 24.39 55,812 24,873 0 2,552 11.33 2
4 Coquimbo 23.0 7 0 2 52 7 18.3 4 80 22 2 1.7 0 1 0 7 1 69 2 1 0 29 27 97 0.8 0.4 0.35 25.45 1 0.00 37 1.50 4 16 7 0 1 21 0 18 0 9 1 0 5 2 1 56.498 0.92 $ 338,014 92.7 71.2 9.7 43.0 7 870 85 6.963 1074 15.50 15.63 24,346 12.00 35.2 2.8 4 9 383.9 750 205,850 25,803 2,786 0.0000 1.912 283.37 116,263 57,234 0 15,265 228.58 1
5 Valparaíso 14.0 37 2 24 161 12 25.7 7 322 56 7 3.2 3 3 1 4 2 48 21 4 46 90 36 720 6.5 2.7 2.11 38.46 13 0.00 59 6.64 13 71 3 0 3 14 3 22 1 0 0 14 5 3 2 53.534 0.99 $ 311,264 92.9 73.5 10.6 27.0 21 3,949 316 12.209 3661 17.21 13.39 44,504 21.18 29.7 2.0 14 27 920.0 257 430,436 106,915 317,309 0.6494 0.197 280.95 316,618 146,161 31 436,195 93.86 1
6 Metropolitana 4.0 56 0 1 404 35 22.5 70 6558 127 25 4.1 30 0 0 3 2 5 9 4 4 85 112 274 6.9 0.9 7.25 47.03 16 0.00 0 0.04 3 3 0 3 4 0 5 9 0 0 0 0 5 0 0 55.901 6.06 $ 421,484 93.1 70.5 11.2 19.0 30 12,881 1261 4.834 15221 38.13 4.33 43,634 9.32 59.8 2.2 15 133 754.2 236 448,887 765,681 1,091,111 1.2810 0.731 541.32 1,306,140 83,459 0 1,147,039 248.50 1
7 O'Higgins 32.0 13 1 9 67 6 21.9 13 251 36 0 1.9 1 7 2 3 16 29 3 1 0 28 15 229 11.3 2.8 0.92 27.15 4 1.28 12 2.47 5 22 1 3 2 2 7 5 2 1 1 7 5 0 0 54.662 0.62 $ 308,068 95.5 65.0 9.5 23.0 7 352 15 9.992 2833 17.46 8.01 14,526 6.34 15.4 2.2 2 7 10.0 0 73,478 7,066 - 0.0000 1.352 134.50 164,204 11,073 0 0 62.12 0
8 Maule 29.0 17 0 1 54 7 11.1 9 657 29 1 1.6 1 7 0 0 10 27 4 3 0 37 28 19 12.7 0.6 0.41 32.86 5 0.00 18 0.82 0 20 23 12 7 5 6 13 3 0 0 9 5 0 0 48.164 0.24 $ 244,231 93.4 73.1 9.0 24.0 4 364 37 8.700 2898 14.49 6.10 12,278 8.67 28.4 2.0 3 14 0.0 0 167,293 8,935 3,942 0.0000 1.480 197.65 185,728 64,500 0 853 232.90 2
9 Biobío 5.0 32 0 0 59 20 18.9 12 488 63 7 3.9 10 0 0 0 4 13 23 2 0 10 54 135 20.7 2.8 0.96 31.68 2 1.07 25 0.73 3 7 19 3 2 4 3 2 0 1 8 0 5 0 0 48.651 1.23 $ 290,367 92.7 71.1 9.9 23.0 17 4,023 320 1.612 2334 16.04 4.93 20,802 7.89 36.0 1.9 5 12 25.0 9 393,481 31,409 1,546 0.0000 1.021 400.89 312,085 49,841 0 828 642.26 1
10 Araucanía 18.0 13 0 2 96 5 13.2 8 179 58 4 30.1 0 5 3 0 13 16 6 10 0 12 31 20 29.4 9.6 0.43 36.79 6 0.00 25 0.16 22 79 51 10 17 3 7 10 0 1 3 23 5 0 1 52.282 2.30 $ 251,081 94.5 72.0 9.1 29.0 8 709 67 9.085 709 11.21 9.90 26,140 16.09 37.4 2.5 15 7 24.0 25 200,377 38,524 130,713 0.0000 1.615 236.97 124,956 228,921 0 112,246 469.74 3
11 Los Ríos 3.0 9 0 1 33 8 16.2 3 94 12 2 16.7 2 1 0 1 0 10 5 2 0 10 19 55 46.1 6.9 0.31 35.41 0 0.00 10 0.14 2 31 13 3 14 4 0 1 0 5 1 0 - 0 1 47.218 0.41 $ 268,648 93.6 64.8 9.3 31.0 4 444 58 8.698 868 17.28 18.91 12,846 11.81 37.9 1.7 9 1 54.1 0 113,900 16,070 11,145 0.0000 2.019 133.02 51,811 376 7 0 42.67 1
12 Los Lagos 1.0 24 1 0 64 11 19.8 8 272 31 1 20.8 2 0 0 0 0 38 3 3 0 57 30 94 56.3 15.9 0.18 34.77 1 0.00 18 0.00 12 41 17 10 9 10 3 6 0 31 0 0 7 3 1 54.555 14.97 $ 291,431 92.9 66.7 9.1 31.0 10 856 72 20.928 2672 14.08 23.97 35,548 23.82 31.6 1.8 27 13 54.1 48 275,043 105,048 259,411 10.9299 1.893 135.48 129,882 486,725 28 192,977 660.16 3
13 Aysén 0.0 4 0 2 17 0 14.8 0 49 12 0 21.8 0 0 0 0 0 24 2 0 1 20 10 0 44.4 39.4 0.02 43.89 0 0.00 0 0.01 21 93 25 8 6 5 23 3 0 8 3 6 6 0 2 61.016 1.05 $ 418,044 95.3 68.5 9.5 66.0 3 64 6 67.765 83 6.83 37.60 8,020 23.06 21.7 1.7 0 1 0.0 19 28,584 10,763 90,876 0.0000 4.089 3.70 19,348 32,030 0 56,201 5.00 4
14 Magallanes y Antártica 0.0 3 0 2 45 6 27.7 2 4 14 0 22.7 3 0 0 0 2 16 16 0 2 24 11 34 20.2 57.3 0.04 40.58 3 0.00 0 0.01 19 62 23 10 0 17 20 10 0 0 1 1 5 1 1 51.872 0.06 $ 572,203 93.6 77.6 10.2 60.0 6 380 55 137.244 222 12.94 41.53 12,436 67.76 37.1 2.0 5 3 0.0 12 84,034 109,092 262,190 0.1650 3.632 6.02 46,336 283,629 18 388,598 0.00 5
In [13]:
# Print dimensions
chile_data_1.shape
Out[13]:
(15, 82)
In [14]:
# Rename the first column
chile_data_1 = chile_data_1.rename(columns={'VARIABLE': 'Region'})
In [15]:
# Print
chile_data_1
Out[15]:
Region CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
0 Arica y Parinacota 31.0 2 0 30 28 4 22.1 2 105 12 2 25.4 1 0 0 0 4 32 4 3 9 32 7 59 - 21.9 0.46 58.00 1 0.00 5 - 13 13 4 6 6 2 0 8 2 0 0 2 - 0 1 42.556 0.88 $ 293,648 94.1 83.8 11.1 53.0 2 193 11 20.038 356 23.69 18.74 6,544 33.01 37.0 2.3 5 3 0.0 0 97,454 34,186 1,151,575 5.2730 2.129 17.35 45,248 15,045 13 167,211 4.67 3
1 Tarapacá 0.0 5 1 13 73 5 20.8 2 178 12 1 12.6 1 0 0 0 0 34 10 1 6 34 12 0 0.2 9.1 0.03 76.03 1 0.00 16 - 2 6 1 1 7 5 0 5 6 4 2 3 5 0 0 68.563 1.45 $ 381,466 91.7 66.7 10.7 42.0 5 255 19 22.180 380 23.03 22.17 11,108 41.43 42.8 2.1 5 10 0.0 11 235,365 40,919 19,560 4.1850 4.021 15.90 81,182 17,161 0 434,727 184.10 1
2 Antofagasta 1.0 9 0 28 81 16 27.4 8 203 15 2 5.7 2 1 0 3 1 24 37 0 4 31 26 63 - 2.8 0.03 39.56 2 0.00 22 - 5 28 8 13 3 14 1 6 10 6 4 0 5 0 0 54.486 1.08 $ 475,866 91.7 66.7 10.6 40.0 7 529 47 20.446 184 24.55 22.76 19,920 35.02 44.6 1.8 2 10 17.0 15 413,922 84,195 22,898 2.0244 3.332 52.25 112,607 315,888 0 115,100 23.00 5
3 Atacama 8.0 10 0 8 35 7 20.0 0 144 12 0 7.5 0 0 0 0 0 18 5 0 2 29 7 33 - 2.0 0.02 40.76 0 3.93 17 NaN 7 23 2 7 2 10 0 3 13 1 3 2 5 1 0 51.844 0.94 $ 379,971 89.1 73.2 10.3 47.0 1 108 16 18.479 578 19.43 18.49 9,674 29.25 36.0 1.9 1 3 0.0 15 168,508 14,222 2,416 0.0000 3.635 24.39 55,812 24,873 0 2,552 11.33 2
4 Coquimbo 23.0 7 0 2 52 7 18.3 4 80 22 2 1.7 0 1 0 7 1 69 2 1 0 29 27 97 0.8 0.4 0.35 25.45 1 0.00 37 1.50 4 16 7 0 1 21 0 18 0 9 1 0 5 2 1 56.498 0.92 $ 338,014 92.7 71.2 9.7 43.0 7 870 85 6.963 1074 15.50 15.63 24,346 12.00 35.2 2.8 4 9 383.9 750 205,850 25,803 2,786 0.0000 1.912 283.37 116,263 57,234 0 15,265 228.58 1
5 Valparaíso 14.0 37 2 24 161 12 25.7 7 322 56 7 3.2 3 3 1 4 2 48 21 4 46 90 36 720 6.5 2.7 2.11 38.46 13 0.00 59 6.64 13 71 3 0 3 14 3 22 1 0 0 14 5 3 2 53.534 0.99 $ 311,264 92.9 73.5 10.6 27.0 21 3,949 316 12.209 3661 17.21 13.39 44,504 21.18 29.7 2.0 14 27 920.0 257 430,436 106,915 317,309 0.6494 0.197 280.95 316,618 146,161 31 436,195 93.86 1
6 Metropolitana 4.0 56 0 1 404 35 22.5 70 6558 127 25 4.1 30 0 0 3 2 5 9 4 4 85 112 274 6.9 0.9 7.25 47.03 16 0.00 0 0.04 3 3 0 3 4 0 5 9 0 0 0 0 5 0 0 55.901 6.06 $ 421,484 93.1 70.5 11.2 19.0 30 12,881 1261 4.834 15221 38.13 4.33 43,634 9.32 59.8 2.2 15 133 754.2 236 448,887 765,681 1,091,111 1.2810 0.731 541.32 1,306,140 83,459 0 1,147,039 248.50 1
7 O'Higgins 32.0 13 1 9 67 6 21.9 13 251 36 0 1.9 1 7 2 3 16 29 3 1 0 28 15 229 11.3 2.8 0.92 27.15 4 1.28 12 2.47 5 22 1 3 2 2 7 5 2 1 1 7 5 0 0 54.662 0.62 $ 308,068 95.5 65.0 9.5 23.0 7 352 15 9.992 2833 17.46 8.01 14,526 6.34 15.4 2.2 2 7 10.0 0 73,478 7,066 - 0.0000 1.352 134.50 164,204 11,073 0 0 62.12 0
8 Maule 29.0 17 0 1 54 7 11.1 9 657 29 1 1.6 1 7 0 0 10 27 4 3 0 37 28 19 12.7 0.6 0.41 32.86 5 0.00 18 0.82 0 20 23 12 7 5 6 13 3 0 0 9 5 0 0 48.164 0.24 $ 244,231 93.4 73.1 9.0 24.0 4 364 37 8.700 2898 14.49 6.10 12,278 8.67 28.4 2.0 3 14 0.0 0 167,293 8,935 3,942 0.0000 1.480 197.65 185,728 64,500 0 853 232.90 2
9 Biobío 5.0 32 0 0 59 20 18.9 12 488 63 7 3.9 10 0 0 0 4 13 23 2 0 10 54 135 20.7 2.8 0.96 31.68 2 1.07 25 0.73 3 7 19 3 2 4 3 2 0 1 8 0 5 0 0 48.651 1.23 $ 290,367 92.7 71.1 9.9 23.0 17 4,023 320 1.612 2334 16.04 4.93 20,802 7.89 36.0 1.9 5 12 25.0 9 393,481 31,409 1,546 0.0000 1.021 400.89 312,085 49,841 0 828 642.26 1
10 Araucanía 18.0 13 0 2 96 5 13.2 8 179 58 4 30.1 0 5 3 0 13 16 6 10 0 12 31 20 29.4 9.6 0.43 36.79 6 0.00 25 0.16 22 79 51 10 17 3 7 10 0 1 3 23 5 0 1 52.282 2.30 $ 251,081 94.5 72.0 9.1 29.0 8 709 67 9.085 709 11.21 9.90 26,140 16.09 37.4 2.5 15 7 24.0 25 200,377 38,524 130,713 0.0000 1.615 236.97 124,956 228,921 0 112,246 469.74 3
11 Los Ríos 3.0 9 0 1 33 8 16.2 3 94 12 2 16.7 2 1 0 1 0 10 5 2 0 10 19 55 46.1 6.9 0.31 35.41 0 0.00 10 0.14 2 31 13 3 14 4 0 1 0 5 1 0 - 0 1 47.218 0.41 $ 268,648 93.6 64.8 9.3 31.0 4 444 58 8.698 868 17.28 18.91 12,846 11.81 37.9 1.7 9 1 54.1 0 113,900 16,070 11,145 0.0000 2.019 133.02 51,811 376 7 0 42.67 1
12 Los Lagos 1.0 24 1 0 64 11 19.8 8 272 31 1 20.8 2 0 0 0 0 38 3 3 0 57 30 94 56.3 15.9 0.18 34.77 1 0.00 18 0.00 12 41 17 10 9 10 3 6 0 31 0 0 7 3 1 54.555 14.97 $ 291,431 92.9 66.7 9.1 31.0 10 856 72 20.928 2672 14.08 23.97 35,548 23.82 31.6 1.8 27 13 54.1 48 275,043 105,048 259,411 10.9299 1.893 135.48 129,882 486,725 28 192,977 660.16 3
13 Aysén 0.0 4 0 2 17 0 14.8 0 49 12 0 21.8 0 0 0 0 0 24 2 0 1 20 10 0 44.4 39.4 0.02 43.89 0 0.00 0 0.01 21 93 25 8 6 5 23 3 0 8 3 6 6 0 2 61.016 1.05 $ 418,044 95.3 68.5 9.5 66.0 3 64 6 67.765 83 6.83 37.60 8,020 23.06 21.7 1.7 0 1 0.0 19 28,584 10,763 90,876 0.0000 4.089 3.70 19,348 32,030 0 56,201 5.00 4
14 Magallanes y Antártica 0.0 3 0 2 45 6 27.7 2 4 14 0 22.7 3 0 0 0 2 16 16 0 2 24 11 34 20.2 57.3 0.04 40.58 3 0.00 0 0.01 19 62 23 10 0 17 20 10 0 0 1 1 5 1 1 51.872 0.06 $ 572,203 93.6 77.6 10.2 60.0 6 380 55 137.244 222 12.94 41.53 12,436 67.76 37.1 2.0 5 3 0.0 12 84,034 109,092 262,190 0.1650 3.632 6.02 46,336 283,629 18 388,598 0.00 5
In [17]:
# Types of columns
chile_data_1.dtypes
Out[17]:
Region                                                                        object
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                float64
NUMBER OF CULTURAL CENTERS                                                     int64
WORLD CULTURAL HERITAGE SITES                                                  int64
NUMBER OF ARCHEOLOGICAL SITES                                                  int64
NATIONAL MONUMENTS                                                             int64
MUSEUMS                                                                        int64
% OF POPULATION THAT ATTENDS MUSEUMS                                         float64
THEATERS                                                                       int64
NUMBER OF THEATER PLAYS PER YEAR                                               int64
LIBRARIES                                                                      int64
GALERIES                                                                       int64
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            float64
NUMBER OF EXHIBITS                                                             int64
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR              int64
MAJOR SPORTS EVENTS PER YEAR                                                   int64
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                              int64
ARTWORK SITES                                                                  int64
POPULAR ARCHITECTURE SITES                                                     int64
HISTORICAL SITES                                                               int64
LOCAL MARKETS                                                                  int64
CULTURAL SITES LEVEL III (INTERNATIONAL)                                       int64
CULTURA SITES LEVEL II (NATIONAL)                                              int64
CULTURAL SITES LEVEL I (LOCAL)                                                 int64
HERITAGE ARCHITECTURAL HOUSES                                                  int64
% OF LAND THAT CORRESPONDS TO FORESTS                                         object
NATIONAL PROTECTED SITES (%)                                                 float64
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 float64
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         float64
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                   int64
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   float64
NUMBER OF BEACHES AND BEACH RESORTS                                            int64
LAND AFFECTED BY WILDFIRES                                                    object
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                              int64
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                    int64
RIVERS, LAKES AND WATERFALLS                                                   int64
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                      int64
GEISERS AND THERMAL CENTERS                                                    int64
PIERS AND SEASHORES                                                            int64
GLACIERS AND WINTER VACATION LOCATIONS                                         int64
VALLEYS                                                                        int64
DESERTS AND DUNES                                                              int64
ISLANDS AND PENINSULAS                                                         int64
PALEONTOLOGY SITES                                                             int64
HIKING TRAILS                                                                  int64
PRESERVED SITES                                                               object
SEASHORE PROTECTED SITES                                                       int64
BIOSHPERE RESERVES                                                             int64
% AVAILABLE WORKFORCE                                                        float64
% POPULATION ORIENTED TOWARDS TOURISM                                        float64
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                        object
5 POPULATION WITH PRIMARY EDUCATION                                          float64
% POPULATION WITH SECONDARY EDUCATION                                        float64
AVERAGE NUMBER OF YEARS STUDYING                                             float64
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  float64
TOURISM-ORIENTED INSTITUTIONS                                                  int64
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                        object
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                        int64
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            float64
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                      int64
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     float64
ROOMS PER 1000 HABITANTS                                                     float64
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                        object
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             float64
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   float64
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 float64
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                  int64
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS             int64
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    float64
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                   int64
NATIONAL TOURISTS ARRIVALS                                                    object
INTERNATIONAL TOURISTS ARRIVALS                                               object
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                 object
DENSITY OF AIRPORTS                                                          float64
DENSITY OF ROADS AND HIGHWAYS                                                float64
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    float64
NUMBER OF VEHICLES                                                            object
VISITORS TO PROTECTED SITES                                                   object
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                         int64
TOURIST'S ARRIVALS THROUGH BORDER LINES                                       object
SECONDARY ROADS (KMS)                                                        float64
NUMBER OF INTERNATIONAL BORDER GATES                                           int64
dtype: object
In [18]:
# Remove $ symbol
chile_data_1 = chile_data_1.replace(r'[<$]', '', regex = True)
In [19]:
# Remove commas from numbers
chile_data_1 = chile_data_1.replace(',','', regex = True)
In [20]:
# Remove `-` character
chile_data_1 = chile_data_1.replace('-','', regex = True)
In [21]:
# Replace empty values with NaNs
chile_data_1 = chile_data_1.replace(r'^\s*$', np.nan, regex = True)
In [23]:
# Check NaNs in the dataset 
chile_data_1
Out[23]:
Region CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
0 Arica y Parinacota 31.0 2 0 30 28 4 22.1 2 105 12 2 25.4 1 0 0 0 4 32 4 3 9 32 7 59 NaN 21.9 0.46 58.00 1 0.00 5 NaN 13 13 4 6 6 2 0 8 2 0 0 2 NaN 0 1 42.556 0.88 293648 94.1 83.8 11.1 53.0 2 193 11 20.038 356 23.69 18.74 6544 33.01 37.0 2.3 5 3 0.0 0 97454 34186 1151575 5.2730 2.129 17.35 45248 15045 13 167211 4.67 3
1 Tarapacá 0.0 5 1 13 73 5 20.8 2 178 12 1 12.6 1 0 0 0 0 34 10 1 6 34 12 0 0.2 9.1 0.03 76.03 1 0.00 16 NaN 2 6 1 1 7 5 0 5 6 4 2 3 5 0 0 68.563 1.45 381466 91.7 66.7 10.7 42.0 5 255 19 22.180 380 23.03 22.17 11108 41.43 42.8 2.1 5 10 0.0 11 235365 40919 19560 4.1850 4.021 15.90 81182 17161 0 434727 184.10 1
2 Antofagasta 1.0 9 0 28 81 16 27.4 8 203 15 2 5.7 2 1 0 3 1 24 37 0 4 31 26 63 NaN 2.8 0.03 39.56 2 0.00 22 NaN 5 28 8 13 3 14 1 6 10 6 4 0 5 0 0 54.486 1.08 475866 91.7 66.7 10.6 40.0 7 529 47 20.446 184 24.55 22.76 19920 35.02 44.6 1.8 2 10 17.0 15 413922 84195 22898 2.0244 3.332 52.25 112607 315888 0 115100 23.00 5
3 Atacama 8.0 10 0 8 35 7 20.0 0 144 12 0 7.5 0 0 0 0 0 18 5 0 2 29 7 33 NaN 2.0 0.02 40.76 0 3.93 17 NaN 7 23 2 7 2 10 0 3 13 1 3 2 5 1 0 51.844 0.94 379971 89.1 73.2 10.3 47.0 1 108 16 18.479 578 19.43 18.49 9674 29.25 36.0 1.9 1 3 0.0 15 168508 14222 2416 0.0000 3.635 24.39 55812 24873 0 2552 11.33 2
4 Coquimbo 23.0 7 0 2 52 7 18.3 4 80 22 2 1.7 0 1 0 7 1 69 2 1 0 29 27 97 0.8 0.4 0.35 25.45 1 0.00 37 1.50 4 16 7 0 1 21 0 18 0 9 1 0 5 2 1 56.498 0.92 338014 92.7 71.2 9.7 43.0 7 870 85 6.963 1074 15.50 15.63 24346 12.00 35.2 2.8 4 9 383.9 750 205850 25803 2786 0.0000 1.912 283.37 116263 57234 0 15265 228.58 1
5 Valparaíso 14.0 37 2 24 161 12 25.7 7 322 56 7 3.2 3 3 1 4 2 48 21 4 46 90 36 720 6.5 2.7 2.11 38.46 13 0.00 59 6.64 13 71 3 0 3 14 3 22 1 0 0 14 5 3 2 53.534 0.99 311264 92.9 73.5 10.6 27.0 21 3949 316 12.209 3661 17.21 13.39 44504 21.18 29.7 2.0 14 27 920.0 257 430436 106915 317309 0.6494 0.197 280.95 316618 146161 31 436195 93.86 1
6 Metropolitana 4.0 56 0 1 404 35 22.5 70 6558 127 25 4.1 30 0 0 3 2 5 9 4 4 85 112 274 6.9 0.9 7.25 47.03 16 0.00 0 0.04 3 3 0 3 4 0 5 9 0 0 0 0 5 0 0 55.901 6.06 421484 93.1 70.5 11.2 19.0 30 12881 1261 4.834 15221 38.13 4.33 43634 9.32 59.8 2.2 15 133 754.2 236 448887 765681 1091111 1.2810 0.731 541.32 1306140 83459 0 1147039 248.50 1
7 O'Higgins 32.0 13 1 9 67 6 21.9 13 251 36 0 1.9 1 7 2 3 16 29 3 1 0 28 15 229 11.3 2.8 0.92 27.15 4 1.28 12 2.47 5 22 1 3 2 2 7 5 2 1 1 7 5 0 0 54.662 0.62 308068 95.5 65.0 9.5 23.0 7 352 15 9.992 2833 17.46 8.01 14526 6.34 15.4 2.2 2 7 10.0 0 73478 7066 NaN 0.0000 1.352 134.50 164204 11073 0 0 62.12 0
8 Maule 29.0 17 0 1 54 7 11.1 9 657 29 1 1.6 1 7 0 0 10 27 4 3 0 37 28 19 12.7 0.6 0.41 32.86 5 0.00 18 0.82 0 20 23 12 7 5 6 13 3 0 0 9 5 0 0 48.164 0.24 244231 93.4 73.1 9.0 24.0 4 364 37 8.700 2898 14.49 6.10 12278 8.67 28.4 2.0 3 14 0.0 0 167293 8935 3942 0.0000 1.480 197.65 185728 64500 0 853 232.90 2
9 Biobío 5.0 32 0 0 59 20 18.9 12 488 63 7 3.9 10 0 0 0 4 13 23 2 0 10 54 135 20.7 2.8 0.96 31.68 2 1.07 25 0.73 3 7 19 3 2 4 3 2 0 1 8 0 5 0 0 48.651 1.23 290367 92.7 71.1 9.9 23.0 17 4023 320 1.612 2334 16.04 4.93 20802 7.89 36.0 1.9 5 12 25.0 9 393481 31409 1546 0.0000 1.021 400.89 312085 49841 0 828 642.26 1
10 Araucanía 18.0 13 0 2 96 5 13.2 8 179 58 4 30.1 0 5 3 0 13 16 6 10 0 12 31 20 29.4 9.6 0.43 36.79 6 0.00 25 0.16 22 79 51 10 17 3 7 10 0 1 3 23 5 0 1 52.282 2.30 251081 94.5 72.0 9.1 29.0 8 709 67 9.085 709 11.21 9.90 26140 16.09 37.4 2.5 15 7 24.0 25 200377 38524 130713 0.0000 1.615 236.97 124956 228921 0 112246 469.74 3
11 Los Ríos 3.0 9 0 1 33 8 16.2 3 94 12 2 16.7 2 1 0 1 0 10 5 2 0 10 19 55 46.1 6.9 0.31 35.41 0 0.00 10 0.14 2 31 13 3 14 4 0 1 0 5 1 0 NaN 0 1 47.218 0.41 268648 93.6 64.8 9.3 31.0 4 444 58 8.698 868 17.28 18.91 12846 11.81 37.9 1.7 9 1 54.1 0 113900 16070 11145 0.0000 2.019 133.02 51811 376 7 0 42.67 1
12 Los Lagos 1.0 24 1 0 64 11 19.8 8 272 31 1 20.8 2 0 0 0 0 38 3 3 0 57 30 94 56.3 15.9 0.18 34.77 1 0.00 18 0.00 12 41 17 10 9 10 3 6 0 31 0 0 7 3 1 54.555 14.97 291431 92.9 66.7 9.1 31.0 10 856 72 20.928 2672 14.08 23.97 35548 23.82 31.6 1.8 27 13 54.1 48 275043 105048 259411 10.9299 1.893 135.48 129882 486725 28 192977 660.16 3
13 Aysén 0.0 4 0 2 17 0 14.8 0 49 12 0 21.8 0 0 0 0 0 24 2 0 1 20 10 0 44.4 39.4 0.02 43.89 0 0.00 0 0.01 21 93 25 8 6 5 23 3 0 8 3 6 6 0 2 61.016 1.05 418044 95.3 68.5 9.5 66.0 3 64 6 67.765 83 6.83 37.60 8020 23.06 21.7 1.7 0 1 0.0 19 28584 10763 90876 0.0000 4.089 3.70 19348 32030 0 56201 5.00 4
14 Magallanes y Antártica 0.0 3 0 2 45 6 27.7 2 4 14 0 22.7 3 0 0 0 2 16 16 0 2 24 11 34 20.2 57.3 0.04 40.58 3 0.00 0 0.01 19 62 23 10 0 17 20 10 0 0 1 1 5 1 1 51.872 0.06 572203 93.6 77.6 10.2 60.0 6 380 55 137.244 222 12.94 41.53 12436 67.76 37.1 2.0 5 3 0.0 12 84034 109092 262190 0.1650 3.632 6.02 46336 283629 18 388598 0.00 5

Now after replacement we have NaNs in 4 columns:

  • % OF LAND THAT CORRESPONDS TO FORESTS (3 NaNs)
  • LAND AFFECTED BY WILDFIRES (4 NaNs)
  • PRESERVED SITES (2 NaNs)
  • NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY (1 NaN)
In [24]:
# Impute data in four columns
imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')
chile_data_1[['% OF LAND THAT CORRESPONDS TO FORESTS', 
            'LAND AFFECTED BY WILDFIRES', 
            'PRESERVED SITES', 
            'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']] = imputer.fit_transform(chile_data_1[['% OF LAND THAT CORRESPONDS TO FORESTS', 
                                                                                                  'LAND AFFECTED BY WILDFIRES', 
            'PRESERVED SITES', 
            'NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY']])
In [25]:
# Check NaNs in the dataset
chile_data_1.isnull().sum(axis = 0)
# As we can see below, we do not have any missing values anymore
Out[25]:
Region                                                                       0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0
NUMBER OF CULTURAL CENTERS                                                   0
WORLD CULTURAL HERITAGE SITES                                                0
NUMBER OF ARCHEOLOGICAL SITES                                                0
NATIONAL MONUMENTS                                                           0
MUSEUMS                                                                      0
% OF POPULATION THAT ATTENDS MUSEUMS                                         0
THEATERS                                                                     0
NUMBER OF THEATER PLAYS PER YEAR                                             0
LIBRARIES                                                                    0
GALERIES                                                                     0
% OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0
NUMBER OF EXHIBITS                                                           0
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0
MAJOR SPORTS EVENTS PER YEAR                                                 0
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0
ARTWORK SITES                                                                0
POPULAR ARCHITECTURE SITES                                                   0
HISTORICAL SITES                                                             0
LOCAL MARKETS                                                                0
CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0
CULTURA SITES LEVEL II (NATIONAL)                                            0
CULTURAL SITES LEVEL I (LOCAL)                                               0
HERITAGE ARCHITECTURAL HOUSES                                                0
% OF LAND THAT CORRESPONDS TO FORESTS                                        0
NATIONAL PROTECTED SITES (%)                                                 0
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0
TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         0
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0
ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   0
NUMBER OF BEACHES AND BEACH RESORTS                                          0
LAND AFFECTED BY WILDFIRES                                                   0
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0
RIVERS, LAKES AND WATERFALLS                                                 0
MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                    0
GEISERS AND THERMAL CENTERS                                                  0
PIERS AND SEASHORES                                                          0
GLACIERS AND WINTER VACATION LOCATIONS                                       0
VALLEYS                                                                      0
DESERTS AND DUNES                                                            0
ISLANDS AND PENINSULAS                                                       0
PALEONTOLOGY SITES                                                           0
HIKING TRAILS                                                                0
PRESERVED SITES                                                              0
SEASHORE PROTECTED SITES                                                     0
BIOSHPERE RESERVES                                                           0
% AVAILABLE WORKFORCE                                                        0
% POPULATION ORIENTED TOWARDS TOURISM                                        0
AVERAGE MONTHLY INCOME (CHILEAN PESOS)                                       0
5 POPULATION WITH PRIMARY EDUCATION                                          0
% POPULATION WITH SECONDARY EDUCATION                                        0
AVERAGE NUMBER OF YEARS STUDYING                                             0
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0
TOURISM-ORIENTED INSTITUTIONS                                                0
NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS                       0
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0
ROOMS PER 1000 HABITANTS                                                     0
NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.                       0
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             0
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   0
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 0
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    0
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)                 0
NATIONAL TOURISTS ARRIVALS                                                   0
INTERNATIONAL TOURISTS ARRIVALS                                              0
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                                0
DENSITY OF AIRPORTS                                                          0
DENSITY OF ROADS AND HIGHWAYS                                                0
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                    0
NUMBER OF VEHICLES                                                           0
VISITORS TO PROTECTED SITES                                                  0
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                       0
TOURIST'S ARRIVALS THROUGH BORDER LINES                                      0
SECONDARY ROADS (KMS)                                                        0
NUMBER OF INTERNATIONAL BORDER GATES                                         0
dtype: int64
In [26]:
# Let's check the correlation
cor = chile_data_1.corr()
cor.loc[:,:] = np.tril(cor, k=-1) 
cor = cor.stack()
cor[(cor > 0.55) | (cor < -0.55)]
# We can see a lot of correlated variables here
Out[26]:
NATIONAL MONUMENTS                                                         NUMBER OF CULTURAL CENTERS                                                   0.844259
MUSEUMS                                                                    NUMBER OF CULTURAL CENTERS                                                   0.863417
                                                                           NATIONAL MONUMENTS                                                           0.842485
% OF POPULATION THAT ATTENDS MUSEUMS                                       NUMBER OF ARCHEOLOGICAL SITES                                                0.563521
THEATERS                                                                   NUMBER OF CULTURAL CENTERS                                                   0.810156
                                                                           NATIONAL MONUMENTS                                                           0.943688
                                                                           MUSEUMS                                                                      0.872746
NUMBER OF THEATER PLAYS PER YEAR                                           NUMBER OF CULTURAL CENTERS                                                   0.771981
                                                                           NATIONAL MONUMENTS                                                           0.938400
                                                                           MUSEUMS                                                                      0.839970
                                                                           THEATERS                                                                     0.982734
LIBRARIES                                                                  NUMBER OF CULTURAL CENTERS                                                   0.916630
                                                                           NATIONAL MONUMENTS                                                           0.897171
                                                                           MUSEUMS                                                                      0.829503
                                                                           THEATERS                                                                     0.893389
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.844666
GALERIES                                                                   NUMBER OF CULTURAL CENTERS                                                   0.865912
                                                                           NATIONAL MONUMENTS                                                           0.953502
                                                                           MUSEUMS                                                                      0.893275
                                                                           THEATERS                                                                     0.942789
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.942964
                                                                           LIBRARIES                                                                    0.926493
NUMBER OF EXHIBITS                                                         NUMBER OF CULTURAL CENTERS                                                   0.822328
                                                                           NATIONAL MONUMENTS                                                           0.899347
                                                                           MUSEUMS                                                                      0.917726
                                                                           THEATERS                                                                     0.954800
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.955996
                                                                           LIBRARIES                                                                    0.869963
                                                                           GALERIES                                                                     0.958262
ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR          CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0.689568
MAJOR SPORTS EVENTS PER YEAR                                               ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0.668614
OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                          % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                           -0.586896
ARTWORK SITES                                                              CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0.706580
                                                                           ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0.879726
                                                                           MAJOR SPORTS EVENTS PER YEAR                                                 0.779169
POPULAR ARCHITECTURE SITES                                                 OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0.606588
HISTORICAL SITES                                                           % OF POPULATION THAT ATTENDS MUSEUMS                                         0.614430
LOCAL MARKETS                                                              MAJOR SPORTS EVENTS PER YEAR                                                 0.682234
CULTURAL SITES LEVEL III (INTERNATIONAL)                                   WORLD CULTURAL HERITAGE SITES                                                0.726786
                                                                           NUMBER OF ARCHEOLOGICAL SITES                                                0.565346
CULTURA SITES LEVEL II (NATIONAL)                                          NUMBER OF CULTURAL CENTERS                                                   0.714245
                                                                           WORLD CULTURAL HERITAGE SITES                                                0.585161
                                                                           NATIONAL MONUMENTS                                                           0.730111
                                                                           THEATERS                                                                     0.567716
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.583450
                                                                           LIBRARIES                                                                    0.555219
                                                                           GALERIES                                                                     0.603571
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.658129
CULTURAL SITES LEVEL I (LOCAL)                                             NUMBER OF CULTURAL CENTERS                                                   0.908111
                                                                           NATIONAL MONUMENTS                                                           0.904602
                                                                           MUSEUMS                                                                      0.932003
                                                                           THEATERS                                                                     0.931128
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.901929
                                                                           LIBRARIES                                                                    0.944968
                                                                           GALERIES                                                                     0.957579
                                                                           NUMBER OF EXHIBITS                                                           0.937123
HERITAGE ARCHITECTURAL HOUSES                                              NUMBER OF CULTURAL CENTERS                                                   0.640105
                                                                           WORLD CULTURAL HERITAGE SITES                                                0.726194
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.869237
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.745822
% OF LAND THAT CORRESPONDS TO FORESTS                                      % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.608867
NATIONAL PROTECTED SITES (%)                                               % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.664562
% LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                               NUMBER OF CULTURAL CENTERS                                                   0.852563
                                                                           NATIONAL MONUMENTS                                                           0.968412
                                                                           MUSEUMS                                                                      0.839991
                                                                           THEATERS                                                                     0.961513
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.961879
                                                                           LIBRARIES                                                                    0.902624
                                                                           GALERIES                                                                     0.970843
                                                                           NUMBER OF EXHIBITS                                                           0.940131
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.682977
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.910700
NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                               NUMBER OF CULTURAL CENTERS                                                   0.822546
                                                                           NATIONAL MONUMENTS                                                           0.888001
                                                                           MUSEUMS                                                                      0.657818
                                                                           THEATERS                                                                     0.761523
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.736817
                                                                           LIBRARIES                                                                    0.850517
                                                                           GALERIES                                                                     0.813042
                                                                           NUMBER OF EXHIBITS                                                           0.698575
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.777774
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.763989
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.723040
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.852965
NUMBER OF BEACHES AND BEACH RESORTS                                        WORLD CULTURAL HERITAGE SITES                                                0.564417
                                                                           POPULAR ARCHITECTURE SITES                                                   0.611618
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.657358
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.630881
LAND AFFECTED BY WILDFIRES                                                 WORLD CULTURAL HERITAGE SITES                                                0.772021
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.893984
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.861942
                                                                           NUMBER OF BEACHES AND BEACH RESORTS                                          0.773169
NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                          % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.757051
                                                                           NATIONAL PROTECTED SITES (%)                                                 0.702737
NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.856296
RIVERS, LAKES AND WATERFALLS                                               % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.581572
                                                                           LOCAL MARKETS                                                                0.564126
                                                                           NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.582272
                                                                           NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0.613234
GEISERS AND THERMAL CENTERS                                                % OF POPULATION THAT ATTENDS MUSEUMS                                        -0.606273
                                                                           % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.582967
                                                                           LOCAL MARKETS                                                                0.679535
                                                                           RIVERS, LAKES AND WATERFALLS                                                 0.579717
PIERS AND SEASHORES                                                        POPULAR ARCHITECTURE SITES                                                   0.601955
GLACIERS AND WINTER VACATION LOCATIONS                                     NATIONAL PROTECTED SITES (%)                                                 0.795207
                                                                           NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.644091
                                                                           NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0.675970
VALLEYS                                                                    OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0.597619
                                                                           POPULAR ARCHITECTURE SITES                                                   0.642939
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.626057
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.555263
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.594042
                                                                           NUMBER OF BEACHES AND BEACH RESORTS                                          0.657487
                                                                           LAND AFFECTED BY WILDFIRES                                                   0.625926
DESERTS AND DUNES                                                          ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                   0.634685
ISLANDS AND PENINSULAS                                                     % OF LAND THAT CORRESPONDS TO FORESTS                                        0.602973
HIKING TRAILS                                                              ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR            0.659527
                                                                           MAJOR SPORTS EVENTS PER YEAR                                                 0.834715
                                                                           ARTWORK SITES                                                                0.618133
                                                                           LOCAL MARKETS                                                                0.744123
                                                                           NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0.588355
                                                                           RIVERS, LAKES AND WATERFALLS                                                 0.578786
PRESERVED SITES                                                            % OF LAND THAT CORRESPONDS TO FORESTS                                        0.761995
                                                                           ISLANDS AND PENINSULAS                                                       0.900599
SEASHORE PROTECTED SITES                                                   WORLD CULTURAL HERITAGE SITES                                                0.589369
                                                                           POPULAR ARCHITECTURE SITES                                                   0.635884
                                                                           NUMBER OF BEACHES AND BEACH RESORTS                                          0.601448
                                                                           PIERS AND SEASHORES                                                          0.652696
                                                                           VALLEYS                                                                      0.567317
BIOSHPERE RESERVES                                                         NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.703191
                                                                           NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                  0.782184
% POPULATION ORIENTED TOWARDS TOURISM                                      ISLANDS AND PENINSULAS                                                       0.829687
                                                                           PRESERVED SITES                                                              0.799716
5 POPULATION WITH PRIMARY EDUCATION                                        ENVIRONMENTAL ISSUES PER MILLION HABITANTS                                  -0.569371
                                                                           DESERTS AND DUNES                                                           -0.756879
AVERAGE NUMBER OF YEARS STUDYING                                           NUMBER OF ARCHEOLOGICAL SITES                                                0.627887
                                                                           % OF POPULATION THAT ATTENDS MUSEUMS                                         0.699837
                                                                           TOXIC WASTE DISPOSAL (TONS/100 hab.)                                         0.611759
                                                                           RIVERS, LAKES AND WATERFALLS                                                -0.634409
HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                NUMBER OF CULTURAL CENTERS                                                  -0.706994
                                                                           MUSEUMS                                                                     -0.582289
                                                                           LIBRARIES                                                                   -0.661580
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                              -0.618349
                                                                           NATIONAL PROTECTED SITES (%)                                                 0.755444
TOURISM-ORIENTED INSTITUTIONS                                              NUMBER OF CULTURAL CENTERS                                                   0.946453
                                                                           NATIONAL MONUMENTS                                                           0.877200
                                                                           MUSEUMS                                                                      0.864319
                                                                           THEATERS                                                                     0.807503
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.762069
                                                                           LIBRARIES                                                                    0.918401
                                                                           GALERIES                                                                     0.893880
                                                                           NUMBER OF EXHIBITS                                                           0.839887
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.701367
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.909699
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.683748
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.867091
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.852548
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                 -0.595463
AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                    NUMBER OF CULTURAL CENTERS                                                   0.875130
                                                                           NATIONAL MONUMENTS                                                           0.953913
                                                                           MUSEUMS                                                                      0.902225
                                                                           THEATERS                                                                     0.954728
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.959102
                                                                           LIBRARIES                                                                    0.912114
                                                                           GALERIES                                                                     0.989141
                                                                           NUMBER OF EXHIBITS                                                           0.976702
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.631276
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.954548
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.979264
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.802655
                                                                           TOURISM-ORIENTED INSTITUTIONS                                                0.897154
DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                          NATIONAL PROTECTED SITES (%)                                                 0.937671
                                                                           NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.573443
                                                                           GLACIERS AND WINTER VACATION LOCATIONS                                       0.782452
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0.714893
CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                  NUMBER OF CULTURAL CENTERS                                                   0.873165
                                                                           NATIONAL MONUMENTS                                                           0.946348
                                                                           MUSEUMS                                                                      0.846557
                                                                           THEATERS                                                                     0.974387
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.967363
                                                                           LIBRARIES                                                                    0.894293
                                                                           GALERIES                                                                     0.934461
                                                                           NUMBER OF EXHIBITS                                                           0.931883
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.690370
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.919552
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.976382
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.814796
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                 -0.558648
                                                                           TOURISM-ORIENTED INSTITUTIONS                                                0.841684
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.957844
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                   NATIONAL MONUMENTS                                                           0.737713
                                                                           MUSEUMS                                                                      0.738595
                                                                           THEATERS                                                                     0.737251
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.756636
                                                                           GALERIES                                                                     0.719589
                                                                           NUMBER OF EXHIBITS                                                           0.728251
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.616409
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.727710
                                                                           NATURAL PROTECTED SITES LEVEL II (NATIONAL)                                 -0.667438
                                                                           RIVERS, LAKES AND WATERFALLS                                                -0.638534
                                                                           AVERAGE NUMBER OF YEARS STUDYING                                             0.757377
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.709608
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0.696218
ROOMS PER 1000 HABITANTS                                                   NUMBER OF CULTURAL CENTERS                                                  -0.588783
                                                                           LIBRARIES                                                                   -0.621327
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                              -0.554075
                                                                           NATIONAL PROTECTED SITES (%)                                                 0.848860
                                                                           NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.551912
                                                                           GLACIERS AND WINTER VACATION LOCATIONS                                       0.584152
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0.870295
                                                                           DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0.845965
TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                           % OF POPULATION THAT ATTENDS MUSEUMS                                         0.572066
                                                                           NATIONAL PROTECTED SITES (%)                                                 0.718213
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0.697431
                                                                           DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0.800771
                                                                           ROOMS PER 1000 HABITANTS                                                     0.785480
AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                 NATIONAL MONUMENTS                                                           0.632719
                                                                           MUSEUMS                                                                      0.683133
                                                                           THEATERS                                                                     0.594317
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.648942
                                                                           GALERIES                                                                     0.674487
                                                                           NUMBER OF EXHIBITS                                                           0.666660
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.610419
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.561508
                                                                           AVERAGE NUMBER OF YEARS STUDYING                                             0.595236
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.643566
                                                                           % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0.757038
AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                               CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR                                0.599157
                                                                           % OF LAND THAT CORRESPONDS TO FORESTS                                       -0.571184
NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION              LOCAL MARKETS                                                                0.589931
                                                                           ISLANDS AND PENINSULAS                                                       0.577918
                                                                           PRESERVED SITES                                                              0.550789
                                                                           % POPULATION ORIENTED TOWARDS TOURISM                                        0.834428
NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS         NUMBER OF CULTURAL CENTERS                                                   0.826176
                                                                           NATIONAL MONUMENTS                                                           0.975323
                                                                           MUSEUMS                                                                      0.858148
                                                                           THEATERS                                                                     0.975050
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.986928
                                                                           LIBRARIES                                                                    0.871143
                                                                           GALERIES                                                                     0.960217
                                                                           NUMBER OF EXHIBITS                                                           0.946637
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.687789
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.919301
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.979435
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.808640
                                                                           TOURISM-ORIENTED INSTITUTIONS                                                0.832542
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.974671
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0.977689
                                                                           % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0.758104
                                                                           AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR                                   0.644461
TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS  NUMBER OF CULTURAL CENTERS                                                   0.730785
                                                                           NATIONAL MONUMENTS                                                           0.745783
                                                                           MUSEUMS                                                                      0.560161
                                                                           THEATERS                                                                     0.561341
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.567172
                                                                           LIBRARIES                                                                    0.650548
                                                                           GALERIES                                                                     0.699662
                                                                           OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0.634816
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.707349
                                                                           CULTURA SITES LEVEL II (NATIONAL)                                            0.831304
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.633719
                                                                           HERITAGE ARCHITECTURAL HOUSES                                                0.848293
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.730927
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.828897
                                                                           LAND AFFECTED BY WILDFIRES                                                   0.607023
                                                                           VALLEYS                                                                      0.677079
                                                                           TOURISM-ORIENTED INSTITUTIONS                                                0.799911
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.701268
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0.666246
                                                                           NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0.676287
TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR)               OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS                            0.851766
                                                                           POPULAR ARCHITECTURE SITES                                                   0.696802
                                                                           PIERS AND SEASHORES                                                          0.562667
                                                                           VALLEYS                                                                      0.660357
                                                                           AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND                                 0.654014
                                                                           TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    0.598853
NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY                              NATIONAL MONUMENTS                                                           0.565476
                                                                           THEATERS                                                                     0.582208
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.606793
                                                                           GALERIES                                                                     0.591355
                                                                           NUMBER OF EXHIBITS                                                           0.571064
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.641856
                                                                           AVERAGE NUMBER OF YEARS STUDYING                                             0.607522
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.572282
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0.582579
                                                                           % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR                     0.618334
                                                                           NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0.601784
DENSITY OF AIRPORTS                                                        ISLANDS AND PENINSULAS                                                       0.757488
                                                                           PRESERVED SITES                                                              0.733560
                                                                           % POPULATION ORIENTED TOWARDS TOURISM                                        0.802385
                                                                           NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0.613059
DENSITY OF ROADS AND HIGHWAYS                                              NUMBER OF CULTURAL CENTERS                                                  -0.725310
                                                                           LIBRARIES                                                                   -0.693484
                                                                           LOCAL MARKETS                                                               -0.562029
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                              -0.603210
                                                                           HERITAGE ARCHITECTURAL HOUSES                                               -0.667809
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                -0.658522
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0.810277
                                                                           TOURISM-ORIENTED INSTITUTIONS                                               -0.674614
                                                                           DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0.574429
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                   -0.556551
                                                                           ROOMS PER 1000 HABITANTS                                                     0.778561
                                                                           TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             0.676773
                                                                           TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS   -0.605450
% OF ROADS THAT ARE HIGHWAYS (FOUR LINES)                                  NUMBER OF CULTURAL CENTERS                                                   0.862005
                                                                           NATIONAL MONUMENTS                                                           0.732969
                                                                           MUSEUMS                                                                      0.781299
                                                                           THEATERS                                                                     0.749427
                                                                           NUMBER OF THEATER PLAYS PER YEAR                                             0.690804
                                                                           LIBRARIES                                                                    0.898907
                                                                           GALERIES                                                                     0.819953
                                                                           NUMBER OF EXHIBITS                                                           0.747979
                                                                           CULTURAL SITES LEVEL I (LOCAL)                                               0.900965
                                                                           % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS                                 0.764096
                                                                           NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED                                 0.705455
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                 -0.736414
                                                                           TOURISM-ORIENTED INSTITUTIONS                                                0.855927
                                                                           AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS                      0.801267
                                                                           CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS                    0.770424
                                                                           ROOMS PER 1000 HABITANTS                                                    -0.731045
                                                                           TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                            -0.643715
                                                                           NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS           0.724497
                                                                           TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS    0.658017
                                                                           DENSITY OF ROADS AND HIGHWAYS                                               -0.802719
NUMBER OF CRUISES THAT ARRIVE PER YEAR                                     WORLD CULTURAL HERITAGE SITES                                                0.612924
                                                                           CULTURAL SITES LEVEL III (INTERNATIONAL)                                     0.618247
                                                                           SEASHORE PROTECTED SITES                                                     0.768414
                                                                           BIOSHPERE RESERVES                                                           0.574067
                                                                           NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0.611306
SECONDARY ROADS (KMS)                                                      % POPULATION ORIENTED TOWARDS TOURISM                                        0.623938
                                                                           NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION                0.622606
NUMBER OF INTERNATIONAL BORDER GATES                                       % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP                            0.561363
                                                                           NATIONAL PROTECTED SITES (%)                                                 0.673851
                                                                           NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL)                            0.602904
                                                                           MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS                                    0.799663
                                                                           HIGHER EDUCATION AND TECHNICAL INSTITUTIONS                                  0.639010
                                                                           DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS)                            0.664923
                                                                           ROOMS PER 1000 HABITANTS                                                     0.710062
                                                                           TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES)                             0.666603
                                                                           DENSITY OF ROADS AND HIGHWAYS                                                0.567876
dtype: float64
In [27]:
# Heatmap of correlations
sns.set(font_scale = 1.2)
f, ax = plt.subplots(figsize = (22, 22))
ax.set_xticklabels(ax.get_xmajorticklabels(), fontsize = 18)
ax.set_yticklabels(ax.get_ymajorticklabels(), fontsize = 18)
sns.heatmap(chile_data_1.drop('Region', axis = 1).corr(), annot = True, 
            linewidths = 0.5, fmt = '.1f', ax = ax, cmap = "YlGnBu")
Out[27]:
<matplotlib.axes._subplots.AxesSubplot at 0x2097d460220>
In [28]:
# Convert object columns to numeric

# Select columns
cols = ['AVERAGE MONTHLY INCOME (CHILEAN PESOS)',
          'NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS',
          'NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC.',
          'NATIONAL TOURISTS ARRIVALS',
          'INTERNATIONAL TOURISTS ARRIVALS',
          'NUMBER OF VEHICLES',
          'VISITORS TO PROTECTED SITES',
          "TOURIST'S ARRIVALS THROUGH BORDER LINES"]

# Convert
chile_data_1[cols] = chile_data_1[cols].apply(pd.to_numeric, errors = 'coerce', axis = 1)
# Now all our columns are integers or floats

2. Exploratory data analysis

In [29]:
plt.barh(sorted(chile_data_1.Region, reverse = True), chile_data_1.MUSEUMS)
plt.title('The number of museums in Chile by region')
plt.show()
In [30]:
plt.scatter(chile_data_1.LIBRARIES, chile_data_1.GALERIES)
plt.title('Correlation between the number of libraries and galeries in Chile by region')
pass

3. Principal Component Analysis

In [31]:
# We need to standardize data for applying PCA

# Create a copy
chile_data_s_1 = chile_data_1.copy()

# Standardize
scaler = StandardScaler()
chile_data_s_1.loc[:, chile_data_s_1.columns != 'Region'] = scaler.fit_transform(chile_data_s_1.loc[:, 
                                                        chile_data_s_1.columns != 'Region'])

# Set region as an index column
chile_data_s_1 = chile_data_s_1.set_index('Region')
In [32]:
chile_data_s_1
Out[32]:
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
Region
Arica y Parinacota 1.672984 -0.959349 -0.559017 2.121142 -0.617597 -0.721316 0.440365 -0.475143 -0.335687 -0.724558 -0.261024 1.389477 -0.368577 -0.668153 -0.454859 -0.679900 0.067176 0.326242 -0.616670 0.294619 0.360486 -0.136165 -0.834058 -0.355859 0.000000 0.643982 -0.248227 1.426648 -0.576151 -0.410550 -0.836955 0.000000 0.597479 -0.772105 -0.685892 0.015686 0.100452 -0.947748 -0.755610 -0.011653 -0.118729 -0.580615 -0.855528 -0.386889 1.657385e-15 -0.620174 0.476731 -1.851488 -0.362785 -0.632189 0.646530 2.619732 1.554178 1.133129 -0.884585 -0.477620 -0.477498 -0.135893 -0.524853 0.790598 0.091521 -1.132711 0.620125 0.166320 0.816497 -0.347974 -0.437516 -0.521005 -0.482777 -0.920689 -0.322261 2.514051e+00 1.231771 -0.062345 -0.946158 -0.519001 -0.762949 0.621100 -0.126593 -0.862524 0.528271
Tarapacá -0.955183 -0.754748 1.118034 0.467040 -0.126575 -0.599746 0.164252 -0.475143 -0.289792 -0.724558 -0.424164 0.064193 -0.368577 -0.668153 -0.454859 -0.679900 -0.738938 0.453349 0.000000 -0.508888 0.094554 -0.051062 -0.638575 -0.688420 -1.306936 -0.162045 -0.490079 2.901672 -0.576151 -0.410550 -0.106280 0.000000 -0.942896 -1.025452 -0.911185 -1.160793 0.315706 -0.451833 -0.755610 -0.536045 0.898951 -0.060661 0.095059 -0.230042 -4.306269e-01 -0.620174 -0.953463 2.567121 -0.207694 0.357940 -0.936809 -0.869163 0.995791 0.344242 -0.494327 -0.458377 -0.451687 -0.072248 -0.518277 0.696840 0.413159 -0.752896 1.147544 0.759346 0.136083 -0.347974 -0.216654 -0.521005 -0.425756 0.095203 -0.285528 -6.097864e-01 0.863508 1.513222 -0.955497 -0.401928 -0.747731 -0.614762 0.777892 -0.044781 -0.792406
Antofagasta -0.870404 -0.481948 -0.559017 1.926542 -0.039282 0.737525 1.566057 -0.112746 -0.274074 -0.626053 -0.261024 -0.650217 -0.233732 -0.267261 -0.454859 0.777029 -0.537409 -0.182187 2.775014 -0.910642 -0.082735 -0.178716 -0.091225 -0.333313 0.000000 -0.558761 -0.490079 -0.081919 -0.360095 -0.410550 0.292270 0.000000 -0.522794 -0.229219 -0.385501 1.662758 -0.545311 1.035911 -0.610300 -0.361247 1.916631 0.199315 1.045645 -0.700582 -4.306269e-01 -0.620174 -0.953463 0.175428 -0.308367 1.422279 -0.936809 -0.869163 0.856194 0.200808 -0.234155 -0.373331 -0.361350 -0.123770 -0.571981 0.912767 0.468484 -0.019562 0.746029 0.943388 -0.884538 -0.771186 -0.216654 -0.461755 -0.405021 1.410506 -0.049429 -6.005751e-01 0.132194 0.939456 -0.721374 -0.299545 1.400719 -0.614762 -0.302782 -0.778986 1.848947
Atacama -0.276947 -0.413748 -0.559017 -0.019460 -0.541216 -0.356606 -0.005664 -0.595941 -0.311168 -0.724558 -0.587304 -0.463849 -0.503423 -0.668153 -0.454859 -0.679900 -0.738938 -0.563509 -0.513892 -0.910642 -0.260023 -0.263819 -0.834058 -0.502412 0.000000 -0.609138 -0.495703 0.016253 -0.792208 3.443259 -0.039855 0.000000 -0.242726 -0.410181 -0.836088 0.250982 -0.760565 0.374691 -0.755610 -0.885639 2.679891 -0.450626 0.570352 -0.386889 -4.306269e-01 0.310087 -0.953463 -0.273450 -0.346460 0.341084 -2.652094 0.457025 0.437403 0.702827 -1.014671 -0.504003 -0.461366 -0.182215 -0.464025 0.185433 0.068078 -0.872233 0.384602 0.064074 -0.544331 -0.912257 -0.437516 -0.521005 -0.405021 -0.397285 -0.431178 -6.570959e-01 -0.553020 1.191780 -0.900815 -0.484583 -0.692266 -0.614762 -0.683313 -0.832171 -0.132068
Coquimbo 0.994747 -0.618348 -0.559017 -0.603261 -0.355718 -0.356606 -0.366735 -0.354344 -0.351405 -0.396208 -0.261024 -1.064369 -0.503423 -0.267261 -0.454859 2.719600 -0.537409 2.677726 -0.822226 -0.508888 -0.437311 -0.263819 -0.052129 -0.141667 -1.269757 -0.709891 -0.310096 -1.236250 -0.576151 -0.410550 1.288645 0.223127 -0.662828 -0.663528 -0.460599 -1.396089 -0.975820 2.193046 -0.755610 1.736318 -0.627570 0.589280 -0.380235 -0.700582 -4.306269e-01 1.240347 0.476731 0.517268 -0.351901 -0.131972 -0.277084 0.048967 -0.400178 0.415959 -0.234155 -0.267490 -0.238749 -0.524389 -0.328122 -0.372854 -0.200109 0.348769 -0.695918 -0.017723 2.517531 -0.489045 -0.248206 0.816999 3.405012 -0.122213 -0.367996 -6.560749e-01 -0.553020 -0.243053 0.767224 -0.287633 -0.459525 -0.614762 -0.640329 0.157934 -0.792406
Valparaíso 0.231731 1.427656 2.795085 1.537341 0.833647 0.251245 1.204986 -0.173145 -0.199258 0.720180 0.554676 -0.909062 -0.098887 0.534522 0.682288 1.262672 -0.335881 1.343100 1.130561 0.696373 3.640321 2.331823 0.299739 3.369953 -0.916559 -0.565058 0.679811 -0.171909 2.016529 -0.410550 2.749996 3.392878 0.597479 1.327055 -0.760990 -1.396089 -0.545311 1.035911 -0.319681 2.435507 -0.373149 -0.580615 -0.855528 1.495273 -4.306269e-01 2.170608 1.906925 0.013683 -0.332855 -0.433572 -0.145139 0.518234 0.856194 -0.731513 1.587050 0.688186 0.506535 -0.368515 0.380711 -0.129935 -0.410158 2.026315 -0.120893 -0.580075 -0.204124 0.921662 0.319723 2.685464 0.849439 1.532153 0.074523 2.118631e-01 -0.333213 -1.671223 0.751637 0.365125 0.180040 2.332293 0.782855 -0.456045 -0.792406
Metropolitana -0.616065 2.723459 -0.559017 -0.700561 3.485167 3.047358 0.525323 3.632022 3.721352 3.051461 3.491193 -0.815878 3.541940 -0.668153 -0.454859 0.777029 -0.335881 -1.389706 -0.102778 0.696373 -0.082735 2.119066 3.271070 0.856016 -0.891773 -0.678406 3.570787 0.529198 2.664699 -0.410550 -1.169080 -0.677230 -0.802862 -1.134029 -0.986283 -0.690201 -0.330057 -1.278358 -0.029062 0.163144 -0.627570 -0.580615 -0.855528 -0.700582 -4.306269e-01 -0.620174 -0.953463 0.415838 1.046635 0.809134 -0.013194 -0.093853 1.693775 -1.305249 2.757824 3.460544 3.555423 -0.587648 3.548131 2.841909 -1.259731 1.953914 -0.863790 2.497524 0.476290 1.062732 3.664195 2.107602 0.740581 1.668068 3.668526 2.347199e+00 -0.119431 -1.226533 2.428629 3.588997 -0.270914 -0.614762 3.186253 0.248718 -0.792406
O'Higgins 1.757764 -0.209147 1.118034 0.077840 -0.192044 -0.478176 0.397886 0.189252 -0.243896 0.063481 -0.587304 -1.043661 -0.368577 2.138090 1.819435 0.777029 2.485518 0.135581 -0.719448 -0.508888 -0.437311 -0.306371 -0.521286 0.602368 -0.619129 -0.558761 0.010499 -1.097174 0.072019 0.844635 -0.371980 0.821310 -0.522794 -0.446373 -0.911185 -0.690201 -0.760565 -0.947748 0.261557 -0.536045 -0.118729 -0.450626 -0.380235 0.397345 -4.306269e-01 -0.620174 -0.953463 0.205331 -0.433528 -0.469607 1.570145 -1.216013 -0.679371 -1.018381 -0.234155 -0.428269 -0.464593 -0.434389 0.153841 -0.094421 -0.914651 -0.468450 -1.050453 -2.042190 0.476290 -0.771186 -0.311309 -0.486152 -0.482777 -1.097303 -0.470219 8.031311e-17 -0.553020 -0.709394 -0.191618 -0.131441 -0.791516 -0.614762 -0.691941 -0.600698 -1.452744
Maule 1.503425 0.063653 -0.559017 -0.700561 -0.333895 -0.356606 -1.895977 -0.052346 0.011359 -0.166364 -0.424164 -1.074722 -0.368577 2.138090 -0.454859 -0.679900 1.276347 0.008474 -0.616670 0.294619 -0.437311 0.076593 -0.013032 -0.581324 -0.532379 -0.697297 -0.276349 -0.630042 0.288076 -0.410550 0.026570 -0.196217 -1.222965 -0.518758 0.740964 1.427462 0.315706 -0.451833 0.116248 0.862333 0.135691 -0.580615 -0.855528 0.711039 -4.306269e-01 -0.620174 -0.953463 -0.898684 -0.536922 -1.189355 0.184723 0.436622 -1.377355 -0.946664 -0.624413 -0.424545 -0.393613 -0.472778 0.171651 -0.516332 -1.093755 -0.655528 -0.904505 -0.712994 -0.204124 -0.630116 -0.090448 -0.521005 -0.482777 -0.406235 -0.460022 -6.528849e-01 -0.553020 -0.602802 0.215118 -0.061316 -0.407268 -0.614762 -0.689057 0.177622 -0.132068
Biobío -0.531285 1.086656 -0.559017 -0.797861 -0.279337 1.223806 -0.239298 0.128852 -0.094893 0.950024 0.554676 -0.836585 0.845031 -0.668153 -0.454859 -0.679900 0.067176 -0.881277 1.336118 -0.107134 -0.437311 -1.072298 1.003476 0.072525 -0.036662 -0.558761 0.032997 -0.726577 -0.360095 0.638706 0.491545 -0.251719 -0.802862 -0.989259 0.440573 -0.690201 -0.760565 -0.617138 -0.319681 -1.060436 -0.627570 -0.450626 2.946818 -0.700582 -4.306269e-01 -0.620174 -0.953463 -0.815943 -0.267554 -0.669181 -0.277084 0.028564 -0.120984 -1.018381 1.066706 0.711154 0.519440 -0.683383 0.017116 -0.296143 -1.203468 0.053838 -0.953363 0.064074 -0.544331 -0.347974 -0.153551 -0.433872 -0.436124 1.259932 -0.337412 -6.594967e-01 -0.553020 -0.985035 1.524147 0.350357 -0.512695 -0.614762 -0.689141 2.043257 -0.792406
Araucanía 0.570849 -0.209147 -0.559017 -0.603261 0.124392 -0.599746 -1.449948 -0.112746 -0.289163 0.785850 0.065256 1.876105 -0.503423 1.336306 2.956582 -0.679900 1.880932 -0.690616 -0.411113 3.106895 -0.437311 -0.987195 0.104257 -0.575688 0.502429 -0.130560 -0.265100 -0.308531 0.504132 -0.410550 0.491545 -0.603228 1.857786 1.616595 2.843699 0.956870 2.468250 -0.782443 0.261557 0.337941 -0.627570 -0.450626 0.570352 2.906894 -4.306269e-01 -0.620174 0.476731 -0.199033 0.023581 -1.112122 0.910420 0.212190 -1.237758 -0.588079 -0.104069 -0.317462 -0.296823 -0.461338 -0.428131 -0.982281 -0.737422 0.498065 -0.439725 0.207218 1.496910 1.062732 -0.311309 -0.437358 -0.353184 -0.162528 -0.298595 -3.030555e-01 -0.553020 -0.490380 0.468371 -0.259311 0.775251 -0.614762 -0.312432 1.257007 0.528271
Los Ríos -0.700845 -0.481948 -0.559017 -0.700561 -0.563039 -0.235036 -0.812764 -0.414743 -0.342603 -0.724558 -0.261024 0.488698 -0.233732 -0.267261 -0.454859 -0.194257 -0.738938 -1.071938 -0.513892 -0.107134 -0.437311 -1.072298 -0.364900 -0.378406 1.537238 -0.300581 -0.332594 -0.421428 -0.792208 -0.410550 -0.504830 -0.615562 -0.942896 -0.120641 -0.010013 -0.690201 1.822487 -0.617138 -0.755610 -1.235233 -0.627570 0.069327 -0.380235 -0.700582 1.657385e-15 -0.620174 0.476731 -1.059410 -0.490667 -0.914058 0.316668 -1.256818 -0.958565 -0.444645 -0.624413 -0.399714 -0.325860 -0.472837 -0.384566 -0.119991 0.107463 -0.608259 -0.707819 0.258341 -1.224745 0.216308 -0.500619 -0.332450 -0.482777 -0.799543 -0.421096 -6.330079e-01 -0.553020 -0.153948 -0.201151 -0.497619 -0.868449 0.050702 -0.691941 -0.689341 -0.792406
Los Lagos -0.870404 0.541054 1.118034 -0.797861 -0.224779 0.129675 -0.048143 -0.112746 -0.230693 -0.100694 -0.424164 0.913203 -0.233732 -0.668153 -0.454859 -0.679900 -0.738938 0.707564 -0.719448 0.294619 -0.437311 0.927623 0.065161 -0.158577 2.169276 0.266157 -0.405712 -0.473786 -0.576151 -0.410550 0.026570 -0.701897 0.457445 0.241283 0.290378 0.956870 0.746215 0.374691 -0.319681 -0.361247 -0.627570 3.449024 -0.855528 -0.700582 3.301473e+00 2.170608 0.476731 0.187151 3.470945 -0.657185 -0.145139 -0.869163 -1.237758 -0.444645 0.156103 -0.271835 -0.280691 -0.109448 0.109727 -0.574576 0.581948 1.280998 0.044474 -0.385808 -0.884538 2.755581 -0.122000 -0.332450 -0.233959 0.387483 0.064338 5.209139e-02 3.146505 -0.258875 -0.185306 -0.243263 2.629382 2.047094 -0.039476 2.124835 0.528271
Aysén -0.955183 -0.822948 -0.559017 -0.603261 -0.737624 -1.207597 -1.110116 -0.595941 -0.370895 -0.724558 -0.587304 1.016741 -0.503423 -0.668153 -0.454859 -0.679900 -0.738938 -0.182187 -0.822226 -0.910642 -0.348667 -0.646783 -0.716768 -0.688420 1.431898 1.745972 -0.495703 0.272316 -0.792208 -0.410550 -1.169080 -0.695731 1.717752 2.123289 0.891159 0.486278 0.100452 -0.451833 2.586510 -0.885639 -0.627570 0.459292 0.570352 0.240498 1.435423e+00 -0.620174 1.906925 1.284880 -0.316530 0.770348 1.438200 -0.501911 -0.679371 2.065449 -0.754499 -0.517660 -0.493630 1.282215 -0.599654 -1.604493 1.860058 -1.009879 -0.003132 -1.398041 -1.224745 -1.053328 -0.500619 -0.521005 -0.384286 -1.428005 -0.450049 -4.129872e-01 -0.553020 1.569849 -1.034075 -0.603383 -0.640792 -0.614762 -0.501923 -0.861020 1.188609
Magallanes y Antártica -0.955183 -0.891148 -0.559017 -0.603261 -0.432100 -0.478176 1.629775 -0.475143 -0.399186 -0.658888 -0.587304 1.109925 -0.098887 -0.668153 -0.454859 -0.679900 -0.335881 -0.690616 0.616670 -0.910642 -0.260023 -0.476577 -0.677672 -0.496775 -0.067645 2.873150 -0.484454 0.001527 -0.144038 -0.410550 -1.169080 -0.695731 1.437684 1.001324 0.740964 0.956870 -1.191074 1.531826 2.150581 0.337941 -0.627570 -0.580615 -0.380235 -0.543736 -4.306269e-01 0.310087 0.476731 -0.268692 -0.585898 2.508457 0.316668 1.354752 0.297807 1.635147 -0.364241 -0.419578 -0.335539 3.346638 -0.561569 -0.736521 2.228582 -0.642380 2.796824 0.176544 -0.204124 -0.347974 -0.437516 -0.521005 -0.420572 -1.019545 0.086400 5.976015e-02 -0.497172 1.189281 -1.019132 -0.515456 1.168712 1.096431 0.621928 -0.883807 1.848947

3.1. Eigenvalues and eigenvectors

In [33]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s_1.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)

# Print 
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors 
[[-0.00326345+0.j          0.20824017+0.j         -0.06726017+0.j
  ...  0.10465649+0.06290102j  0.0825293 +0.j
  -0.06441922+0.j        ]
 [ 0.18406724+0.j          0.02020972+0.j          0.00790098+0.j
  ...  0.01160419-0.03416025j -0.06176204+0.j
   0.0253879 +0.j        ]
 [ 0.03833065+0.j          0.12890046+0.j          0.19880302+0.j
  ...  0.0680857 -0.08400698j  0.10493056+0.j
   0.00281664+0.j        ]
 ...
 [ 0.15581808+0.j         -0.13450585+0.j          0.07284215+0.j
  ...  0.06271399+0.12335499j -0.15264865+0.j
  -0.19538787+0.j        ]
 [ 0.05190728+0.j          0.07724022+0.j         -0.05197245+0.j
  ... -0.11024802+0.06092036j  0.06548136+0.j
   0.03261723+0.j        ]
 [-0.09074038+0.j         -0.16095204+0.j          0.08978727+0.j
  ...  0.05759012-0.03912584j -0.2056735 +0.j
  -0.00869763+0.j        ]]

Eigenvalues 
[ 2.69523928e+01+0.00000000e+00j  1.11549471e+01+0.00000000e+00j
  9.96822553e+00+0.00000000e+00j  9.61463611e+00+0.00000000e+00j
  6.68175566e+00+0.00000000e+00j  4.31458703e+00+0.00000000e+00j
  4.23410198e+00+0.00000000e+00j  3.18910023e+00+0.00000000e+00j
  2.65843378e+00+0.00000000e+00j  2.44249475e+00+0.00000000e+00j
  1.56101521e+00+0.00000000e+00j  1.03135070e+00+0.00000000e+00j
  1.22663898e+00+0.00000000e+00j  1.75603441e+00+0.00000000e+00j
 -1.32494920e-15+0.00000000e+00j  1.07942003e-15+2.19847086e-16j
  1.07942003e-15-2.19847086e-16j -1.08558207e-15+7.71474565e-17j
 -1.08558207e-15-7.71474565e-17j  8.71749523e-16+0.00000000e+00j
 -8.76887078e-16+1.29909988e-16j -8.76887078e-16-1.29909988e-16j
 -8.19629336e-16+0.00000000e+00j -8.02835427e-16+0.00000000e+00j
  6.42085919e-16+2.25477533e-16j  6.42085919e-16-2.25477533e-16j
  7.31410794e-16+0.00000000e+00j  6.75212761e-16+9.97428658e-17j
  6.75212761e-16-9.97428658e-17j -6.16515505e-16+1.93020234e-16j
 -6.16515505e-16-1.93020234e-16j  3.37982701e-16+4.58297866e-16j
  3.37982701e-16-4.58297866e-16j -5.40920802e-16+2.46405795e-16j
 -5.40920802e-16-2.46405795e-16j  7.96558550e-19+5.16308390e-16j
  7.96558550e-19-5.16308390e-16j  5.33183976e-16+1.90833605e-16j
  5.33183976e-16-1.90833605e-16j -6.12695112e-16+3.54095714e-17j
 -6.12695112e-16-3.54095714e-17j  5.44210923e-16+0.00000000e+00j
 -4.32965571e-16+2.70857102e-16j -4.32965571e-16-2.70857102e-16j
  3.16516089e-16+3.17529050e-16j  3.16516089e-16-3.17529050e-16j
  4.90988752e-16+0.00000000e+00j  3.26653526e-16+2.29722921e-16j
  3.26653526e-16-2.29722921e-16j -4.57634772e-16+8.29659489e-17j
 -4.57634772e-16-8.29659489e-17j  4.31151588e-16+0.00000000e+00j
 -3.89811887e-16+1.55411110e-16j -3.89811887e-16-1.55411110e-16j
  3.62866586e-16+1.35658313e-16j  3.62866586e-16-1.35658313e-16j
 -4.10026407e-16+0.00000000e+00j -1.56552748e-16+2.73925233e-16j
 -1.56552748e-16-2.73925233e-16j -2.11588101e-16+1.70609867e-16j
 -2.11588101e-16-1.70609867e-16j  3.85920978e-17+2.51322967e-16j
  3.85920978e-17-2.51322967e-16j  7.81144645e-17+2.48725863e-16j
  7.81144645e-17-2.48725863e-16j  2.26001301e-16+1.39886408e-16j
  2.26001301e-16-1.39886408e-16j -6.68674770e-17+1.87675760e-16j
 -6.68674770e-17-1.87675760e-16j -2.53426156e-16+6.02657924e-17j
 -2.53426156e-16-6.02657924e-17j  2.74138192e-16+0.00000000e+00j
 -8.60230185e-17+1.23946681e-16j -8.60230185e-17-1.23946681e-16j
 -2.36499805e-16+0.00000000e+00j  7.46164047e-17+8.22653204e-17j
  7.46164047e-17-8.22653204e-17j  1.64379319e-16+6.55327386e-17j
  1.64379319e-16-6.55327386e-17j  2.16307442e-16+0.00000000e+00j
 -1.45515467e-17+0.00000000e+00j]
In [34]:
# Run PCA and fit the model
myPCA = PCA()
x = myPCA.fit(chile_data_s_1)

# Plotting the varaince explained by each component
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
pass
In [35]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [36]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[36]:
array([0.31056255, 0.43909692, 0.55395713, 0.66474306, 0.74173449,
       0.79144989, 0.8402379 , 0.87698473, 0.90761689, 0.93576086,
       0.95599501, 0.97398201, 0.98811612, 1.        , 1.        ])

will use only first 7 components, which explain 84% of variance.

In [37]:
# Calculate loadings
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_1)

# Print
print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are 
 [[-3.26344655e-03  1.84067239e-01  3.83306466e-02 -7.23394920e-03
   1.89367967e-01  1.77718705e-01  4.28888502e-02  1.80816408e-01
   1.75744035e-01  1.83273803e-01  1.89226250e-01 -9.19233750e-02
   1.76736744e-01 -2.33968407e-03  3.44343546e-03  8.24000942e-02
   1.57056623e-04 -2.43602829e-02  3.35780487e-02  6.56190691e-02
   6.30485406e-02  1.46874937e-01  1.86464208e-01  1.19261160e-01
  -8.06108890e-02 -9.44997273e-02  1.89172194e-01 -2.84345477e-03
   1.72731180e-01 -2.65307899e-02  3.39580094e-02  4.57788989e-02
  -6.73488248e-02 -6.92156625e-02 -6.72298110e-02 -7.84966666e-02
  -2.81542402e-02 -4.29849694e-02 -5.47581663e-02  6.40633176e-02
  -4.27780975e-02 -3.18548698e-02 -3.52051754e-02 -6.30454443e-03
  -4.07223535e-02  1.97763697e-02 -4.31746880e-02  6.90941735e-03
   5.62308924e-02 -8.62514507e-03 -1.41202049e-02 -8.72500998e-03
   8.70796084e-02 -1.30382323e-01  1.85947486e-01  1.90520746e-01
   1.88437411e-01 -8.47907807e-02  1.86791012e-01  1.45137261e-01
  -1.22095527e-01  1.57441862e-01 -8.17256553e-02  1.13569258e-01
   4.17261941e-02  8.50647027e-02  1.85656621e-01  1.58968571e-01
   6.89541575e-02  1.47490361e-01  1.73481294e-01  1.05107949e-01
  -3.90739108e-03 -1.35187201e-01  1.71388118e-01  1.90228008e-01
  -5.07229601e-03  8.52520439e-03  1.55818082e-01  5.19072846e-02
  -9.07403778e-02]
 [-2.08240165e-01 -2.02097221e-02 -1.28900460e-01  7.74734314e-04
   5.30581710e-02  8.25267486e-02  1.23135676e-01  7.48874275e-02
   1.03220440e-01 -1.54906591e-02  6.19211778e-02  8.12451535e-02
   1.16894851e-01 -2.25797775e-01 -1.86706465e-01 -1.07869203e-01
  -1.80189332e-01 -1.51918353e-01  6.63902463e-02 -1.41930363e-01
  -9.54951885e-02 -5.28439294e-03  3.61324473e-02 -1.24732327e-01
   5.17274035e-02  1.59198578e-01  5.32312233e-02  1.55010260e-01
  -4.15627603e-02  5.35670380e-03 -2.19020425e-01 -1.73893343e-01
   2.11969599e-02 -3.89465539e-02 -5.42563793e-02  8.45072909e-02
  -7.12537301e-02 -8.09121618e-03  9.03703911e-02 -1.50497141e-01
   7.28692103e-02  1.47887169e-02  3.35097324e-02 -1.91659117e-01
   4.06755844e-02 -8.77120365e-02 -5.76503597e-02  5.87925994e-02
   3.94811941e-02  2.37121544e-01 -7.92609574e-02  2.80317499e-02
   1.53466243e-01  1.54650405e-01 -1.54682334e-04  6.59307916e-02
   7.83134209e-02  1.68524835e-01  4.72593885e-02  1.18805017e-01
   1.72851125e-01 -6.83313500e-02  1.88016314e-01  1.68548102e-01
  -1.23141097e-01 -3.84432296e-02  8.36974143e-02 -6.09562650e-02
  -9.05656822e-02  8.74288007e-04  1.28298985e-01  8.66347125e-02
   6.04743366e-02  1.89216680e-01 -8.18407607e-02  6.28094248e-02
   4.49248636e-02 -2.81003694e-02  1.34505855e-01 -7.72402159e-02
   1.60952045e-01]
 [-6.72601655e-02  7.90098232e-03  1.98803016e-01  1.31476712e-01
   3.69730413e-03 -3.78539934e-02  1.89921617e-01 -7.47860172e-02
  -6.72295511e-02 -5.13379054e-02 -3.54471625e-02  3.00881006e-02
  -6.24193209e-02 -9.30019512e-02 -4.20568338e-02  1.01573124e-01
  -1.47468750e-01  1.85525172e-01  8.13875229e-02 -4.14386467e-02
   2.29441917e-01  1.76852823e-01 -5.40488637e-02  1.76054194e-01
  -6.62647174e-03  1.04752568e-01 -2.34208709e-02  2.84242076e-02
   5.54101307e-02 -8.59056349e-02  1.48462621e-01  1.82995339e-01
   1.32801884e-01  1.37523500e-01 -6.80081341e-02 -3.71006296e-02
  -9.59559255e-02  2.19110135e-01  3.42115504e-02  1.88197187e-01
  -2.67826407e-02  1.05071846e-01 -1.21039360e-01  4.01040532e-03
   9.50459774e-02  2.63808922e-01  2.07325056e-01  6.38975020e-02
   7.41321871e-02  1.05659647e-01 -3.26727631e-02  6.75573552e-02
   8.88663926e-02  1.02859680e-01  5.02224174e-02 -1.90061348e-02
  -2.55108858e-02  1.18121954e-01 -4.00153294e-02 -4.03368954e-02
   1.56617370e-01  1.34124327e-01  1.57058200e-01 -2.60714401e-02
  -1.22937113e-02  1.08066972e-01 -2.48587318e-02  1.54047690e-01
   1.07766445e-01  6.75968476e-02 -5.74014818e-03  3.18904190e-02
   1.12930008e-01  8.56277664e-03 -6.12990594e-02 -4.54109376e-02
   1.56034894e-01  2.65616594e-01  7.28421502e-02 -5.19724521e-02
   8.97872670e-02]
 [-6.45423962e-02  5.95374302e-02 -1.71041361e-02 -1.62238884e-01
   3.15601573e-02 -2.13512914e-03 -1.24976800e-01  4.69921394e-02
   3.74635389e-02  8.55147505e-02  3.64067372e-02  2.14585936e-01
   3.38010189e-02  1.91064847e-02  1.00173332e-01 -1.36514347e-01
   5.22993453e-02 -9.27257198e-02 -1.01084699e-01  1.88858826e-01
  -6.97056830e-02  1.52660980e-03  6.28418534e-02 -4.50783226e-02
   2.32917753e-01  1.21895705e-01  2.53243417e-02 -5.55061148e-02
   3.68070002e-02 -1.32921127e-01 -8.23128429e-02 -1.40639416e-01
   1.89322840e-01  1.79082873e-01  2.35793220e-01  1.33962694e-01
   2.05918374e-01 -9.41100376e-02  1.44024502e-01 -3.58024399e-02
  -2.03022782e-01  1.58871209e-01 -4.29215340e-02  1.11032143e-01
   2.16521511e-01  3.70873748e-02  1.33192101e-01 -2.82453000e-02
   2.03207597e-01 -6.72388765e-02  1.72390119e-01 -2.64943989e-02
  -1.70382698e-01 -2.87144534e-02  4.56955770e-02  2.82103348e-02
   3.20553141e-02  5.78851272e-02  4.52975095e-02 -1.18673897e-01
   4.70016257e-02  9.55299080e-02 -3.11045462e-02 -2.44852636e-02
  -4.59169810e-02  2.26577087e-01  3.14479942e-02 -3.07741107e-02
  -8.10965789e-02 -3.81898141e-02  5.14639000e-02  4.31272548e-02
   1.14647335e-01 -5.47894180e-02  4.32559716e-02  2.86044583e-02
   1.75886080e-01  1.01358533e-01  3.29161248e-02  1.74510248e-01
   1.00237393e-01]
 [-1.30025827e-01  4.79638221e-02  5.57893243e-02 -6.32476877e-02
  -5.55461595e-02  7.53353756e-02 -2.82412215e-02 -3.28024643e-02
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  -1.37124824e-01]]
In [38]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s_1)
vars = pca.explained_variance_ratio_
c_names = chile_data_s_1.columns
sum = 0

print('Variance:  Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
    output = '{0:4.1f}%:    '.format(100.0 * vars[idx])
    output += " + ".join("{0:5.2f} * {1:s}".format(val, name) \
                      for val, name in zip(row, c_names))
    sum += 100*vars[idx]
    print(output)

print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 84.0%
Variance:  Projected dimension
------------------------------
31.1%:    -0.00 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.18 * NUMBER OF CULTURAL CENTERS +  0.04 * WORLD CULTURAL HERITAGE SITES + -0.01 * NUMBER OF ARCHEOLOGICAL SITES +  0.19 * NATIONAL MONUMENTS +  0.18 * MUSEUMS +  0.04 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.18 * THEATERS +  0.18 * NUMBER OF THEATER PLAYS PER YEAR +  0.18 * LIBRARIES +  0.19 * GALERIES + -0.09 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.18 * NUMBER OF EXHIBITS + -0.00 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.00 * MAJOR SPORTS EVENTS PER YEAR +  0.08 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS +  0.00 * ARTWORK SITES + -0.02 * POPULAR ARCHITECTURE SITES +  0.03 * HISTORICAL SITES +  0.07 * LOCAL MARKETS +  0.06 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.15 * CULTURA SITES LEVEL II (NATIONAL) +  0.19 * CULTURAL SITES LEVEL I (LOCAL) +  0.12 * HERITAGE ARCHITECTURAL HOUSES + -0.08 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.09 * NATIONAL PROTECTED SITES (%) +  0.19 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.17 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.03 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.03 * NUMBER OF BEACHES AND BEACH RESORTS +  0.05 * LAND AFFECTED BY WILDFIRES + -0.07 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.07 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS + -0.08 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.03 * GEISERS AND THERMAL CENTERS + -0.04 * PIERS AND SEASHORES + -0.05 * GLACIERS AND WINTER VACATION LOCATIONS +  0.06 * VALLEYS + -0.04 * DESERTS AND DUNES + -0.03 * ISLANDS AND PENINSULAS + -0.04 * PALEONTOLOGY SITES + -0.01 * HIKING TRAILS + -0.04 * PRESERVED SITES +  0.02 * SEASHORE PROTECTED SITES + -0.04 * BIOSHPERE RESERVES +  0.01 * % AVAILABLE WORKFORCE +  0.06 * % POPULATION ORIENTED TOWARDS TOURISM + -0.01 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.01 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION +  0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.13 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.19 * TOURISM-ORIENTED INSTITUTIONS +  0.19 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.19 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.19 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.15 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.12 * ROOMS PER 1000 HABITANTS +  0.16 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.08 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.11 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.04 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.09 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.19 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.16 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.07 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.15 * NATIONAL TOURISTS ARRIVALS +  0.17 * INTERNATIONAL TOURISTS ARRIVALS +  0.11 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + -0.00 * DENSITY OF AIRPORTS + -0.14 * DENSITY OF ROADS AND HIGHWAYS +  0.17 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.19 * NUMBER OF VEHICLES + -0.01 * VISITORS TO PROTECTED SITES +  0.01 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.16 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.05 * SECONDARY ROADS (KMS) + -0.09 * NUMBER OF INTERNATIONAL BORDER GATES
12.9%:    -0.21 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.02 * NUMBER OF CULTURAL CENTERS + -0.13 * WORLD CULTURAL HERITAGE SITES +  0.00 * NUMBER OF ARCHEOLOGICAL SITES +  0.05 * NATIONAL MONUMENTS +  0.08 * MUSEUMS +  0.12 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.07 * THEATERS +  0.10 * NUMBER OF THEATER PLAYS PER YEAR + -0.02 * LIBRARIES +  0.06 * GALERIES +  0.08 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.12 * NUMBER OF EXHIBITS + -0.23 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.19 * MAJOR SPORTS EVENTS PER YEAR + -0.11 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.18 * ARTWORK SITES + -0.15 * POPULAR ARCHITECTURE SITES +  0.07 * HISTORICAL SITES + -0.14 * LOCAL MARKETS + -0.10 * CULTURAL SITES LEVEL III (INTERNATIONAL) + -0.01 * CULTURA SITES LEVEL II (NATIONAL) +  0.04 * CULTURAL SITES LEVEL I (LOCAL) + -0.12 * HERITAGE ARCHITECTURAL HOUSES +  0.05 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.16 * NATIONAL PROTECTED SITES (%) +  0.05 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.16 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.04 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED +  0.01 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.22 * NUMBER OF BEACHES AND BEACH RESORTS + -0.17 * LAND AFFECTED BY WILDFIRES +  0.02 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.04 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.05 * RIVERS, LAKES AND WATERFALLS +  0.08 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.07 * GEISERS AND THERMAL CENTERS + -0.01 * PIERS AND SEASHORES +  0.09 * GLACIERS AND WINTER VACATION LOCATIONS + -0.15 * VALLEYS +  0.07 * DESERTS AND DUNES +  0.01 * ISLANDS AND PENINSULAS +  0.03 * PALEONTOLOGY SITES + -0.19 * HIKING TRAILS +  0.04 * PRESERVED SITES + -0.09 * SEASHORE PROTECTED SITES + -0.06 * BIOSHPERE RESERVES +  0.06 * % AVAILABLE WORKFORCE +  0.04 * % POPULATION ORIENTED TOWARDS TOURISM +  0.24 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.08 * 5 POPULATION WITH PRIMARY EDUCATION +  0.03 * % POPULATION WITH SECONDARY EDUCATION +  0.15 * AVERAGE NUMBER OF YEARS STUDYING +  0.15 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.00 * TOURISM-ORIENTED INSTITUTIONS +  0.07 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.08 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.17 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.05 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.17 * ROOMS PER 1000 HABITANTS + -0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.19 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.17 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.12 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.04 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.08 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.06 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.09 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.00 * NATIONAL TOURISTS ARRIVALS +  0.13 * INTERNATIONAL TOURISTS ARRIVALS +  0.09 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.06 * DENSITY OF AIRPORTS +  0.19 * DENSITY OF ROADS AND HIGHWAYS + -0.08 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.06 * NUMBER OF VEHICLES +  0.04 * VISITORS TO PROTECTED SITES + -0.03 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.13 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.08 * SECONDARY ROADS (KMS) +  0.16 * NUMBER OF INTERNATIONAL BORDER GATES
11.5%:    -0.07 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.01 * NUMBER OF CULTURAL CENTERS +  0.20 * WORLD CULTURAL HERITAGE SITES +  0.13 * NUMBER OF ARCHEOLOGICAL SITES +  0.00 * NATIONAL MONUMENTS + -0.04 * MUSEUMS +  0.19 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.07 * THEATERS + -0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.04 * GALERIES +  0.03 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.06 * NUMBER OF EXHIBITS + -0.09 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.04 * MAJOR SPORTS EVENTS PER YEAR +  0.10 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.15 * ARTWORK SITES +  0.19 * POPULAR ARCHITECTURE SITES +  0.08 * HISTORICAL SITES + -0.04 * LOCAL MARKETS +  0.23 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.18 * CULTURA SITES LEVEL II (NATIONAL) + -0.05 * CULTURAL SITES LEVEL I (LOCAL) +  0.18 * HERITAGE ARCHITECTURAL HOUSES + -0.01 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.10 * NATIONAL PROTECTED SITES (%) + -0.02 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.03 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.06 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.09 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.15 * NUMBER OF BEACHES AND BEACH RESORTS +  0.18 * LAND AFFECTED BY WILDFIRES +  0.13 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.14 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS + -0.04 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS +  0.22 * PIERS AND SEASHORES +  0.03 * GLACIERS AND WINTER VACATION LOCATIONS +  0.19 * VALLEYS + -0.03 * DESERTS AND DUNES +  0.11 * ISLANDS AND PENINSULAS + -0.12 * PALEONTOLOGY SITES +  0.00 * HIKING TRAILS +  0.10 * PRESERVED SITES +  0.26 * SEASHORE PROTECTED SITES +  0.21 * BIOSHPERE RESERVES +  0.06 * % AVAILABLE WORKFORCE +  0.07 * % POPULATION ORIENTED TOWARDS TOURISM +  0.11 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.03 * 5 POPULATION WITH PRIMARY EDUCATION +  0.07 * % POPULATION WITH SECONDARY EDUCATION +  0.09 * AVERAGE NUMBER OF YEARS STUDYING +  0.10 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.05 * TOURISM-ORIENTED INSTITUTIONS + -0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.12 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.04 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.16 * ROOMS PER 1000 HABITANTS +  0.13 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.16 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.03 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.01 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.15 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.11 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.07 * NATIONAL TOURISTS ARRIVALS + -0.01 * INTERNATIONAL TOURISTS ARRIVALS +  0.03 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.11 * DENSITY OF AIRPORTS +  0.01 * DENSITY OF ROADS AND HIGHWAYS + -0.06 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.05 * NUMBER OF VEHICLES +  0.16 * VISITORS TO PROTECTED SITES +  0.27 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.07 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.05 * SECONDARY ROADS (KMS) +  0.09 * NUMBER OF INTERNATIONAL BORDER GATES
11.1%:    -0.06 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.06 * NUMBER OF CULTURAL CENTERS + -0.02 * WORLD CULTURAL HERITAGE SITES + -0.16 * NUMBER OF ARCHEOLOGICAL SITES +  0.03 * NATIONAL MONUMENTS + -0.00 * MUSEUMS + -0.12 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.05 * THEATERS +  0.04 * NUMBER OF THEATER PLAYS PER YEAR +  0.09 * LIBRARIES +  0.04 * GALERIES +  0.21 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.03 * NUMBER OF EXHIBITS +  0.02 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.10 * MAJOR SPORTS EVENTS PER YEAR + -0.14 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS +  0.05 * ARTWORK SITES + -0.09 * POPULAR ARCHITECTURE SITES + -0.10 * HISTORICAL SITES +  0.19 * LOCAL MARKETS + -0.07 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.00 * CULTURA SITES LEVEL II (NATIONAL) +  0.06 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES +  0.23 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.12 * NATIONAL PROTECTED SITES (%) +  0.03 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.06 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.04 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.13 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.14 * LAND AFFECTED BY WILDFIRES +  0.19 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.18 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) +  0.24 * RIVERS, LAKES AND WATERFALLS +  0.13 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.21 * GEISERS AND THERMAL CENTERS + -0.09 * PIERS AND SEASHORES +  0.14 * GLACIERS AND WINTER VACATION LOCATIONS + -0.04 * VALLEYS + -0.20 * DESERTS AND DUNES +  0.16 * ISLANDS AND PENINSULAS + -0.04 * PALEONTOLOGY SITES +  0.11 * HIKING TRAILS +  0.22 * PRESERVED SITES +  0.04 * SEASHORE PROTECTED SITES +  0.13 * BIOSHPERE RESERVES + -0.03 * % AVAILABLE WORKFORCE +  0.20 * % POPULATION ORIENTED TOWARDS TOURISM + -0.07 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.17 * 5 POPULATION WITH PRIMARY EDUCATION + -0.03 * % POPULATION WITH SECONDARY EDUCATION + -0.17 * AVERAGE NUMBER OF YEARS STUDYING + -0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.05 * TOURISM-ORIENTED INSTITUTIONS +  0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.06 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.05 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.05 * ROOMS PER 1000 HABITANTS +  0.10 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.03 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.02 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.05 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.23 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.03 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.08 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.04 * NATIONAL TOURISTS ARRIVALS +  0.05 * INTERNATIONAL TOURISTS ARRIVALS +  0.04 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.11 * DENSITY OF AIRPORTS + -0.05 * DENSITY OF ROADS AND HIGHWAYS +  0.04 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.03 * NUMBER OF VEHICLES +  0.18 * VISITORS TO PROTECTED SITES +  0.10 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.17 * SECONDARY ROADS (KMS) +  0.10 * NUMBER OF INTERNATIONAL BORDER GATES
 7.7%:    -0.13 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.05 * NUMBER OF CULTURAL CENTERS +  0.06 * WORLD CULTURAL HERITAGE SITES + -0.06 * NUMBER OF ARCHEOLOGICAL SITES + -0.06 * NATIONAL MONUMENTS +  0.08 * MUSEUMS + -0.03 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.03 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.05 * GALERIES + -0.10 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.02 * NUMBER OF EXHIBITS + -0.12 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.15 * MAJOR SPORTS EVENTS PER YEAR +  0.00 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.16 * ARTWORK SITES +  0.07 * POPULAR ARCHITECTURE SITES +  0.02 * HISTORICAL SITES + -0.09 * LOCAL MARKETS + -0.09 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.01 * CULTURA SITES LEVEL II (NATIONAL) +  0.02 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES +  0.13 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.17 * NATIONAL PROTECTED SITES (%) + -0.07 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.05 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.14 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED +  0.07 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.06 * LAND AFFECTED BY WILDFIRES + -0.19 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.16 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.09 * RIVERS, LAKES AND WATERFALLS + -0.01 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.02 * GEISERS AND THERMAL CENTERS +  0.06 * PIERS AND SEASHORES + -0.22 * GLACIERS AND WINTER VACATION LOCATIONS + -0.12 * VALLEYS +  0.10 * DESERTS AND DUNES +  0.29 * ISLANDS AND PENINSULAS +  0.07 * PALEONTOLOGY SITES + -0.19 * HIKING TRAILS +  0.20 * PRESERVED SITES +  0.14 * SEASHORE PROTECTED SITES + -0.12 * BIOSHPERE RESERVES +  0.05 * % AVAILABLE WORKFORCE +  0.24 * % POPULATION ORIENTED TOWARDS TOURISM + -0.10 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.20 * 5 POPULATION WITH PRIMARY EDUCATION + -0.21 * % POPULATION WITH SECONDARY EDUCATION + -0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.11 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.01 * TOURISM-ORIENTED INSTITUTIONS + -0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.04 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.17 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.05 * ROOMS PER 1000 HABITANTS +  0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.09 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.03 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.12 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.14 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.04 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.06 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.02 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.13 * NATIONAL TOURISTS ARRIVALS + -0.04 * INTERNATIONAL TOURISTS ARRIVALS + -0.14 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.22 * DENSITY OF AIRPORTS +  0.00 * DENSITY OF ROADS AND HIGHWAYS +  0.02 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES +  0.12 * VISITORS TO PROTECTED SITES +  0.04 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + -0.09 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.21 * SECONDARY ROADS (KMS) + -0.07 * NUMBER OF INTERNATIONAL BORDER GATES
 5.0%:     0.17 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.05 * NUMBER OF CULTURAL CENTERS +  0.11 * WORLD CULTURAL HERITAGE SITES +  0.25 * NUMBER OF ARCHEOLOGICAL SITES +  0.00 * NATIONAL MONUMENTS + -0.08 * MUSEUMS +  0.01 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.01 * THEATERS +  0.01 * NUMBER OF THEATER PLAYS PER YEAR + -0.06 * LIBRARIES + -0.02 * GALERIES +  0.16 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.04 * NUMBER OF EXHIBITS +  0.00 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.02 * MAJOR SPORTS EVENTS PER YEAR + -0.16 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS +  0.04 * ARTWORK SITES +  0.01 * POPULAR ARCHITECTURE SITES + -0.16 * HISTORICAL SITES +  0.12 * LOCAL MARKETS +  0.08 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.10 * CULTURA SITES LEVEL II (NATIONAL) + -0.09 * CULTURAL SITES LEVEL I (LOCAL) + -0.01 * HERITAGE ARCHITECTURAL HOUSES +  0.02 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.05 * NATIONAL PROTECTED SITES (%) +  0.01 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.29 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.01 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.09 * NUMBER OF BEACHES AND BEACH RESORTS +  0.04 * LAND AFFECTED BY WILDFIRES + -0.02 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.14 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.15 * RIVERS, LAKES AND WATERFALLS + -0.02 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.16 * GEISERS AND THERMAL CENTERS + -0.24 * PIERS AND SEASHORES + -0.21 * GLACIERS AND WINTER VACATION LOCATIONS + -0.05 * VALLEYS +  0.06 * DESERTS AND DUNES +  0.01 * ISLANDS AND PENINSULAS + -0.23 * PALEONTOLOGY SITES +  0.02 * HIKING TRAILS +  0.06 * PRESERVED SITES + -0.05 * SEASHORE PROTECTED SITES + -0.03 * BIOSHPERE RESERVES + -0.11 * % AVAILABLE WORKFORCE +  0.09 * % POPULATION ORIENTED TOWARDS TOURISM + -0.18 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.01 * 5 POPULATION WITH PRIMARY EDUCATION +  0.16 * % POPULATION WITH SECONDARY EDUCATION +  0.16 * AVERAGE NUMBER OF YEARS STUDYING + -0.01 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.09 * TOURISM-ORIENTED INSTITUTIONS + -0.04 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.04 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.13 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.01 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.18 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.06 * ROOMS PER 1000 HABITANTS + -0.07 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.04 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.04 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.03 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.00 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.05 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.16 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.08 * NATIONAL TOURISTS ARRIVALS +  0.00 * INTERNATIONAL TOURISTS ARRIVALS +  0.29 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.28 * DENSITY OF AIRPORTS + -0.03 * DENSITY OF ROADS AND HIGHWAYS + -0.14 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.02 * NUMBER OF VEHICLES + -0.06 * VISITORS TO PROTECTED SITES +  0.12 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.07 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.06 * SECONDARY ROADS (KMS) + -0.06 * NUMBER OF INTERNATIONAL BORDER GATES
 4.9%:     0.12 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.07 * NUMBER OF CULTURAL CENTERS + -0.08 * WORLD CULTURAL HERITAGE SITES + -0.19 * NUMBER OF ARCHEOLOGICAL SITES + -0.01 * NATIONAL MONUMENTS + -0.08 * MUSEUMS + -0.12 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.05 * THEATERS +  0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES +  0.00 * GALERIES + -0.02 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.02 * NUMBER OF EXHIBITS + -0.06 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.15 * MAJOR SPORTS EVENTS PER YEAR +  0.18 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.07 * ARTWORK SITES +  0.22 * POPULAR ARCHITECTURE SITES + -0.36 * HISTORICAL SITES + -0.13 * LOCAL MARKETS + -0.15 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.01 * CULTURA SITES LEVEL II (NATIONAL) + -0.01 * CULTURAL SITES LEVEL I (LOCAL) + -0.09 * HERITAGE ARCHITECTURAL HOUSES + -0.04 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.07 * NATIONAL PROTECTED SITES (%) +  0.05 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.04 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.08 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.09 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.13 * NUMBER OF BEACHES AND BEACH RESORTS + -0.11 * LAND AFFECTED BY WILDFIRES + -0.06 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.12 * RIVERS, LAKES AND WATERFALLS + -0.19 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.07 * GEISERS AND THERMAL CENTERS +  0.05 * PIERS AND SEASHORES +  0.06 * GLACIERS AND WINTER VACATION LOCATIONS +  0.07 * VALLEYS + -0.21 * DESERTS AND DUNES +  0.12 * ISLANDS AND PENINSULAS + -0.24 * PALEONTOLOGY SITES + -0.18 * HIKING TRAILS +  0.11 * PRESERVED SITES +  0.07 * SEASHORE PROTECTED SITES +  0.10 * BIOSHPERE RESERVES +  0.08 * % AVAILABLE WORKFORCE +  0.05 * % POPULATION ORIENTED TOWARDS TOURISM + -0.01 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.14 * 5 POPULATION WITH PRIMARY EDUCATION +  0.00 * % POPULATION WITH SECONDARY EDUCATION + -0.07 * AVERAGE NUMBER OF YEARS STUDYING +  0.12 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.06 * TOURISM-ORIENTED INSTITUTIONS +  0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.04 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.07 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.06 * ROOMS PER 1000 HABITANTS + -0.05 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.09 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.07 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.20 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.03 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.06 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.05 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.29 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.22 * NATIONAL TOURISTS ARRIVALS +  0.05 * INTERNATIONAL TOURISTS ARRIVALS +  0.09 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.03 * DENSITY OF AIRPORTS +  0.02 * DENSITY OF ROADS AND HIGHWAYS +  0.03 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.03 * NUMBER OF VEHICLES + -0.16 * VISITORS TO PROTECTED SITES + -0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.02 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.06 * SECONDARY ROADS (KMS) + -0.14 * NUMBER OF INTERNATIONAL BORDER GATES
Total variance explained by the 7 components 84.0%
In [40]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s_1)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
 The Factor scores are
Out[40]:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
0 -2.386142 1.128396 0.417084 -1.039372 -2.383579 5.560673 0.896446
1 -1.603717 2.205502 0.000489 -3.106017 1.359288 2.146064 -0.009668
2 -1.163348 3.041472 0.920221 -3.063266 1.643872 -1.248495 -3.558966
3 -3.022894 1.686132 -1.287520 -3.948946 1.601311 0.192783 -1.124276
4 0.071800 -3.459654 1.681821 -3.193220 1.184838 -2.570795 4.848208
5 5.666810 -5.185133 7.767526 -1.476143 -1.618477 0.063266 -2.100251
6 16.071582 4.590960 -2.013523 1.269896 -1.218331 0.133082 1.318211
7 -0.533601 -4.130965 -2.604111 -1.666633 -1.291375 0.430387 0.879329
8 -0.906211 -3.216091 -3.517245 0.026947 -0.489855 0.118975 0.163107
9 2.200100 -0.453991 -3.581660 -0.554127 2.180578 -2.483251 -2.047424
10 -1.152331 -4.064711 -2.573562 5.555666 -2.773524 -0.040094 -2.429809
11 -2.270740 -0.234031 -2.562392 0.799648 1.506049 0.456833 0.681446
12 -0.571975 0.292343 3.611886 6.352109 6.280926 1.319659 0.674149
13 -6.015841 2.613051 0.379445 2.969578 -2.191594 -1.956671 1.752658
14 -4.383492 5.186719 3.361541 1.073879 -3.790125 -2.122415 0.056839
In [41]:
#visualize an example how variables can contribute to diffent principal components
# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_1)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
    y_axis[i]=[np.mean(pca_model.components_[i][0:24]), np.mean(pca_model.components_[i][24:47]), 
               np.mean(pca_model.components_[i][47:59]), np.mean(pca_model.components_[i][59:69]),
               np.mean(pca_model.components_[i][69:81])]
# Plot
x_axis = ['CULTURAL HERITAGE', 'NATURAL RESOURCES', 'WORKFORCE DEVELOPMENT', 'TOURISM INFRASTRUCTURE', 'TOURISM MOBILITY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass

4. Developing a scoring system for 5 dimensions

Methodology steps:

Step 1 - Calculate a weighted average for each variable in principal components.

Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.

In [42]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s_1.columns, pca_model.components_[0] * 
                                        pca_model.explained_variance_ratio_[0],
                                        pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
                                        pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
                                        pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
                                        pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
                                        pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
                                        pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)

# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)

# Print
weights.head()
Out[42]:
1 2 3 4 5 6 7 weighted_average
0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR -0.0010135 -0.026766 -0.00772552 -0.00715039 -0.0100109 0.00849465 0.00587723 -0.045576
NUMBER OF CULTURAL CENTERS 0.0571644 -0.00259764 0.000907509 0.00659591 0.0036928 -0.00260661 -0.00364229 0.070830
WORLD CULTURAL HERITAGE SITES 0.0119041 -0.0165681 0.0228346 -0.0018949 0.0042953 0.00555241 -0.003667 0.026726
NUMBER OF ARCHEOLOGICAL SITES -0.00224659 9.958e-05 0.0151014 -0.0179738 -0.00486953 0.0123346 -0.00910945 -0.007931
NATIONAL MONUMENTS 0.0588106 0.0068198 0.000424673 0.00349642 -0.00427658 3.51305e-05 -0.000404655 0.077246

Step 2. Calculate a score for each dimension.

Multiply weighted average of a variable by each standartized value in each column and sum up results, receiving a final score.

In [43]:
# Example
chile_data_s_1.head(1)
Out[43]:
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES
Region
Arica y Parinacota 1.672984 -0.959349 -0.559017 2.121142 -0.617597 -0.721316 0.440365 -0.475143 -0.335687 -0.724558 -0.261024 1.389477 -0.368577 -0.668153 -0.454859 -0.6799 0.067176 0.326242 -0.61667 0.294619 0.360486 -0.136165 -0.834058 -0.355859 0.0 0.643982 -0.248227 1.426648 -0.576151 -0.41055 -0.836955 0.0 0.597479 -0.772105 -0.685892 0.015686 0.100452 -0.947748 -0.75561 -0.011653 -0.118729 -0.580615 -0.855528 -0.386889 1.657385e-15 -0.620174 0.476731 -1.851488 -0.362785 -0.632189 0.64653 2.619732 1.554178 1.133129 -0.884585 -0.47762 -0.477498 -0.135893 -0.524853 0.790598 0.091521 -1.132711 0.620125 0.16632 0.816497 -0.347974 -0.437516 -0.521005 -0.482777 -0.920689 -0.322261 2.514051 1.231771 -0.062345 -0.946158 -0.519001 -0.762949 0.6211 -0.126593 -0.862524 0.528271

As a result, we multiply:

  • weighted average for CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR (-0.045576) in the dataframe weights by each value in the CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR column in the dataframe chile_data_s_1.

  • Do the same for all weighted averages and columns in respective dataframe

  • Sum up the product of multiplications and receive the score for the first dimension

In [44]:
# Ranking for dimension 1: CULTURAL HERITAGE AND EVENTS

# Create a dataframe for relevant variables
dim1 = chile_data_s_1.iloc[:, 0:24].mul(weights['weighted_average'][0:24], axis = 1)

# Create a score ranking
dim1['Ranking 1'] = dim1.sum(axis = 1)

# Sort by score
dim1.sort_values(by = 'Ranking 1', ascending = False).head()
Out[44]:
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES Ranking 1
Region
Metropolitana 0.028078 0.192903 -0.014940 0.005556 0.269217 0.214340 0.018570 0.266501 0.287588 0.180319 0.259461 -0.008320 0.269909 0.03980 0.018823 0.008948 0.019085 -0.001139 0.000772 0.009099 -0.001584 0.180688 0.233427 0.028090 2.505190
Valparaíso -0.010561 0.101121 0.074702 -0.012192 0.064396 0.017672 0.042596 -0.012705 -0.015399 0.042557 0.041223 -0.009270 -0.007536 -0.03184 -0.028235 0.014540 0.019085 0.001101 -0.008496 0.009099 0.069700 0.198829 0.021390 0.110586 0.692363
Biobío 0.024214 0.076968 -0.014940 0.006328 -0.021578 0.086078 -0.008459 0.009455 -0.007333 0.056140 0.041223 -0.008531 0.064394 0.03980 0.018823 -0.007829 -0.003817 -0.000722 -0.010041 -0.001400 -0.008373 -0.091432 0.071609 0.002380 0.312954
Los Lagos 0.039669 0.038323 0.029881 0.006328 -0.017363 0.009121 -0.001702 -0.008273 -0.017828 -0.005950 -0.031523 0.009313 -0.017811 0.03980 0.018823 -0.007829 0.041987 0.000580 0.005406 0.003850 -0.008373 0.079096 0.004650 -0.005204 0.204969
Antofagasta 0.039669 -0.034136 -0.014940 -0.015279 -0.003034 0.051875 0.055359 -0.008273 -0.021181 -0.036995 -0.019399 -0.006631 -0.017811 0.01592 0.018823 0.008948 0.030536 -0.000149 -0.020854 -0.011899 -0.001584 -0.015239 -0.006510 -0.010938 -0.023722
In [45]:
# Ranking for dimension 2: NATURAL RESOURCES AND SUSTAINABILITY

# Create a dataframe for relevant variables
dim2 = chile_data_s_1.iloc[:, 24:47].mul(weights['weighted_average'][24:47], axis = 1)

# Create a score ranking
dim2['Ranking 2'] = dim2.sum(axis = 1)

# Sort the by score
dim2.sort_values(by = 'Ranking 2', ascending = False).head()
Out[45]:
% OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES Ranking 2
Region
Los Lagos 0.039944 0.001468 -0.030505 -0.013954 -0.029854 0.01565 -0.000457 0.008902 -0.000437 -0.004235 -0.010288 -0.015863 0.000245 -0.001953 0.003823 -0.004355 0.021719 0.207479 0.043709 0.030237 2.027671e-01 0.106482 0.006901 0.577425
Metropolitana -0.016421 -0.003741 0.268483 0.015586 0.138076 0.01565 0.020110 0.008589 0.000766 0.019904 0.034944 0.011442 -0.000108 0.006662 0.000348 0.001967 0.021719 -0.034927 0.043709 0.030237 -2.644788e-02 -0.030423 -0.013803 0.512320
Valparaíso -0.016877 -0.003116 0.051114 -0.005063 0.104490 0.01565 -0.047304 -0.043033 -0.000570 -0.023292 0.026962 0.023145 -0.000179 -0.005399 0.003823 0.029363 0.012914 -0.034927 0.043709 -0.064535 -2.644788e-02 0.106482 0.027605 0.174514
Coquimbo -0.023380 -0.003915 -0.023316 -0.036409 -0.029854 0.01565 -0.022167 -0.002830 0.000633 0.011646 0.016319 0.023145 -0.000321 -0.011429 0.009037 0.020934 0.021719 0.035449 0.019426 0.030237 -2.644788e-02 0.060847 0.006901 0.091873
Arica y Parinacota 0.000000 0.003551 -0.018664 0.042017 -0.029854 0.01565 0.014397 -0.000000 -0.000570 0.013552 0.024301 -0.000260 0.000033 0.004939 0.009037 -0.000140 0.004109 -0.034927 0.043709 0.016698 1.017919e-16 -0.030423 0.006901 0.084054
In [46]:
# Ranking for dimension 3: HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT

# Create a dataframe for relevant variables
dim3 = chile_data_s_1.iloc[:, 47:59].mul(weights['weighted_average'][47:59], axis = 1)

# Create a score ranking
dim3['Ranking 3'] = dim3.sum(axis = 1)

# Sort the dataframe by score
dim3.sort_values(by = 'Ranking 3', ascending = False).head()
Out[46]:
% AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS Ranking 3
Region
Metropolitana 0.008077 0.098508 0.014620 0.000113 0.000244 0.072541 0.024056 0.199376 0.265943 0.278337 0.001871 0.281558 1.245244
Los Lagos 0.003635 0.326682 -0.011874 0.001243 0.002256 -0.053011 0.008195 0.011285 -0.020891 -0.021974 0.000349 0.008707 0.254603
Valparaíso 0.000266 -0.031328 -0.007834 0.001243 -0.001345 0.036669 0.013482 0.114735 0.052887 0.039654 0.001174 0.030211 0.249814
Biobío -0.015849 -0.025182 -0.012091 0.002373 -0.000074 -0.005182 0.018769 0.077117 0.054652 0.040665 0.002176 0.001358 0.138733
Tarapacá 0.049863 -0.019548 0.006467 0.008022 0.002256 0.042648 -0.006344 -0.035737 -0.035226 -0.035360 0.000230 -0.041127 -0.063857
In [47]:
# Ranking for dimension 4: TOURISM INFRASTRUCTURE

# Create a dataframe for relevant variables
dim4 = chile_data_s_1.iloc[:, 59:69].mul(weights['weighted_average'][59:69], axis = 1)

# Create a score ranking
dim4['Ranking 4'] = dim4.sum(axis = 1)

# Sort the dataframe by score
dim4.sort_values(by = 'Ranking 4', ascending = False).head()
Out[47]:
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) Ranking 4
Region
Metropolitana 0.176745 -0.005552 0.151894 -0.004538 0.156454 -0.003975 0.093013 0.301501 0.129180 0.018534 1.013256
Valparaíso -0.008081 -0.001808 0.157523 -0.000635 -0.036338 0.001704 0.080666 0.026308 0.164599 0.021258 0.405196
Los Lagos -0.035734 0.002565 0.099583 0.000234 -0.024168 0.007383 0.241175 -0.010038 -0.020377 -0.005855 0.254766
Coquimbo -0.023189 -0.000882 0.027113 -0.003656 -0.001110 -0.021012 -0.042802 -0.020423 0.050076 0.085215 0.049329
Araucanía -0.061090 -0.003250 0.038719 -0.002310 0.012981 -0.012494 0.093013 -0.025615 -0.026807 -0.008839 0.004307
In [48]:
# Ranking for dimension 5: TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE

# Create a dataframe for relevant variables
dim5 = chile_data_s_1.iloc[:, 69:81].mul(weights['weighted_average'][69:81], axis = 1)

# Create a score ranking
dim5['Ranking 5'] = dim5.sum(axis = 1)

# Sort the dataframe by score
dim5.sort_values(by = 'Ranking 5', ascending = False).head()
Out[48]:
NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES Ranking 5
Region
Metropolitana 0.089442 0.326733 0.168270 -0.009218 0.033727 0.105649 0.268562 -0.012999 -0.034666 0.285755 0.008728 0.001532 1.231514
Los Lagos 0.020777 0.005730 0.003734 0.242869 0.007119 -0.008061 -0.018203 0.126167 0.115434 -0.003540 0.074567 -0.001021 0.565572
Valparaíso 0.082154 0.006637 0.015188 -0.025720 0.045955 0.032697 0.027322 0.008639 0.131516 0.070209 -0.016004 0.001532 0.380127
Arica y Parinacota -0.049367 -0.028702 0.180232 0.095077 0.001714 -0.041159 -0.038836 -0.036609 0.035023 -0.011353 -0.030269 -0.001021 0.074729
Biobío 0.067557 -0.030051 -0.047279 -0.042686 0.027087 0.066302 0.026217 -0.024601 -0.034666 -0.061805 0.071704 0.001532 0.019311
In [50]:
# Create an aggregated dataframe with all scores
scoring_data = pd.concat([dim1.iloc[:,-1:], dim2.iloc[:,-1:], dim3.iloc[:,-1:], dim4.iloc[:,-1:], dim5.iloc[:,-1:]], axis=1)


scoring_data
Out[50]:
Ranking 1 Ranking 2 Ranking 3 Ranking 4 Ranking 5
Region
Arica y Parinacota -0.415952 0.084054 -0.227440 -0.142092 0.074729
Tarapacá -0.173136 -0.013629 -0.063857 -0.051780 -0.111462
Antofagasta -0.023722 -0.210598 -0.075573 0.003949 -0.066772
Atacama -0.315089 -0.340057 -0.188900 -0.203309 -0.415831
Coquimbo -0.250916 0.091873 -0.128560 0.049329 -0.217581
Valparaíso 0.692363 0.174514 0.249814 0.405196 0.380127
Metropolitana 2.505190 0.512320 1.245244 1.013256 1.231514
O'Higgins -0.571647 -0.122728 -0.138538 -0.318736 -0.295041
Maule -0.476902 -0.145830 -0.227193 -0.242213 -0.240416
Biobío 0.312954 -0.259354 0.138733 -0.096586 0.019311
Araucanía -0.510915 -0.330403 -0.159923 0.004307 -0.067661
Los Ríos -0.294472 0.045124 -0.245964 -0.086307 -0.333723
Los Lagos 0.204969 0.577425 0.254603 0.254766 0.565572
Aysén -0.441574 -0.012318 -0.253732 -0.422402 -0.465189
Magallanes y Antártica -0.241153 -0.050395 -0.178714 -0.167379 -0.057579
In [51]:
scoring_data.style.highlight_null().render().split('\n')[:10]

def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

scoring_data.style.\
    applymap(color_negative_red).\
    apply(highlight_max)
Out[51]:
Ranking 1 Ranking 2 Ranking 3 Ranking 4 Ranking 5
Region
Arica y Parinacota -0.415952 0.084054 -0.227440 -0.142092 0.074729
Tarapacá -0.173136 -0.013629 -0.063857 -0.051780 -0.111462
Antofagasta -0.023722 -0.210598 -0.075573 0.003949 -0.066772
Atacama -0.315089 -0.340057 -0.188900 -0.203309 -0.415831
Coquimbo -0.250916 0.091873 -0.128560 0.049329 -0.217581
Valparaíso 0.692363 0.174514 0.249814 0.405196 0.380127
Metropolitana 2.505190 0.512320 1.245244 1.013256 1.231514
O'Higgins -0.571647 -0.122728 -0.138538 -0.318736 -0.295041
Maule -0.476902 -0.145830 -0.227193 -0.242213 -0.240416
Biobío 0.312954 -0.259354 0.138733 -0.096586 0.019311
Araucanía -0.510915 -0.330403 -0.159923 0.004307 -0.067661
Los Ríos -0.294472 0.045124 -0.245964 -0.086307 -0.333723
Los Lagos 0.204969 0.577425 0.254603 0.254766 0.565572
Aysén -0.441574 -0.012318 -0.253732 -0.422402 -0.465189
Magallanes y Antártica -0.241153 -0.050395 -0.178714 -0.167379 -0.057579

Reapeat the above for dimensions 6-10

In [52]:
chile_data_2 = pd.read_csv('Tourism Chile D6 - D10 (English).csv', encoding = 'ISO-8859-1', header = 2)
In [53]:
chile_data_2 = chile_data_2[:-3]
chile_data_2 = chile_data_2[:-1]
In [54]:
chile_data_2
Out[54]:
Region Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Number of Winter Centers Number of shopping centers Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Number of Vineyards and Wine Routes Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) FNDR resources allocated to promotion (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
0 Arica y Parinacota 35.857 19.563 12 21.619 43.766 0 5.273 1 20 20.038 5 0 - - 240.55 17 6 - 0.318 0.26 42.184 15.819 0.513 11,884,613 0.426 26,467 2,387 981 319 265 7.3 8.2 84.369 1 11 253 94.915 45,560 137 5.273 6,517 1,060,490 193,364 118,413 92,641 36,370,835 11,757,543 446,922 24,729,573 1
1 Tarapacá 25.947 26.616 2 18.958 60.264 0 4.185 3 9 22.18 5 40.134 - 2 213.07 28 0 - 0.421 0.468 87.884 0 0.516 14,740,693 0.398 76,896 1,862 1,105 593 592 4.8 13.4 485.457 6 14.9 59,745 138.104 83,265 3,236 4.185 7,600 1,750,481 74,404 13,600 - 42,364,100 12,796,789 355,208 29,714,382 2
2 Antofagasta 37.248 35.446 22 29.96 69.233 3 4.049 3 36 20.446 2 14.292 - 3 259.23 39 2 - 0.546 0.314 109.315 0 0.381 22,383,612 0.495 123,017 4,228 2,324 7 765 6.2 7.3 12.146 13 9.6 30,220 178.143 238,010 16,666 4.049 16,280 2,846,340 200,000 149,000 149,744 38,262,875 29,066,423 192,572 50,841,622 1
3 Atacama 44.823 26.933 19 25.478 56.225 1 3.932 1 9 18.479 3 32.437 - - 184.01 35 1 - 0.39 0.197 66.841 0 0.488 17,068,077 0.533 21,705 3,125 965 14 504 6.4 10.2 15.727 3 29.3 17,860 145.477 53,046 3,456 3.932 11,614 556,668 94,100 187,035 - 35,948,639 16,986,593 233,225 30,224,699 2
4 Coquimbo 37.632 25.165 9 17.805 47.745 3 3.316 3 13 6.963 3 14.091 - 1 150.06 72 3 - 0.288 0.218 155.833 0 0.396 20,225,710 0.449 69,974 4,203 1,831 100 1,082 6 12.3 11.605 3 29 1,431 112.73 275,447 2,229 1.658 11,056 5,364,222 189,900 72,917 - 64,917,630 33,222,779 673,800 44,790,979 3
5 Valparaíso 52.408 47.05 16 27.756 55.85 9 1.948 12 44 12.209 3 18.71 2 8 230.65 179 3 5 0.311 0.291 87.671 1.948 0.432 24,137,492 0.445 236,177 19,176 6,765 166 2,761 7.5 11.6 50.654 10 10.7 16,641 143.52 1,557,887 4,287 1.299 5,649 3,058,423 129,481 63,996 1,079,295 101,802,434 24,239,428 811,772 51,647,041 1
6 Metropolitana 9.223 16.914 47 25.508 65.499 0 0 21 42 4.834 4 14.717 5 33 331.61 507 39 1 0.372 0.295 65.334 0.66 0.47 60,400,444 0.424 1,146,510 53,517 23,242 7,605 9,385 7.7 8.8 39.596 102 12.3 147,198 1295.113 7,307,884 6,196 0.33 19,352 13,709,951 56,982 - - 230,562,951 43,552,270 62,052 90,875,903 1
7 O'Higgins 18.959 49.422 6 19.946 43.427 0 2.562 3 39 9.992 2 14.95 1 2 117.3 114 3 7 0.203 0.22 51.241 0 0.396 18,182,163 0.477 111,020 4,894 2,451 762 1,136 6 9.9 11.529 7 10.2 26,831 95.805 265,601 3,056 1.281 4,700 754,000 179,000 6,000 345,000 50,200,910 17,850,727 444,491 43,045,390 1
8 Maule 15.637 19.943 10 23.852 35.899 9 1.101 1 24 8.7 5 25.383 - 3 109.73 130 0 2 0.245 0.227 27.53 1.101 0.427 25,936,948 0.53 124,820 6,018 4,771 194 1,875 6.4 15.8 11.012 5 10.7 1,371 48.346 165,417 1,311 1.101 7,183 647,510 168,850 8,000 218,000 97,730,159 19,292,650 486,500 48,826,193 2
9 Biobío 14.128 14.004 16 27.16 38.194 2 1.612 6 26 1.612 3 20.531 1 3 162.84 211 1 - 0.36 0.31 63.925 0 0.42 41,208,340 0.41 292,281 19,222 5,713 37 3,193 7.9 15.8 12.892 12 11.8 15,518 188.607 305,920 4,475 1.074 7,362 3,548,528 317,000 44,000 319,660 137,998,425 28,228,357 1,646,127 88,172,439 3
10 Araucanía 15.986 15.687 25 25.887 40.826 23 2.3 2 16 9.085 13 22.736 4 1 135.88 118 5 - 0.381 0.288 78.203 1.15 0.369 29,000,528 0.469 140,063 8,685 3,161 323 1,550 8 18.1 20.701 4 30.3 5,856 232.887 395,583 372 1.15 9,488 1,842,375 108,799 27,507 - 67,212,297 31,050,589 - 54,529,735 2
11 Los Ríos 34.512 26.207 9 30.472 42.649 2 2.806 1 40 8.698 12 0 1 - 191.32 44 1 - 0.274 0.27 101.011 0 0.298 15,834,434 0.44 22,076 3,235 818 29 773 6.4 14.3 11.223 4 11.3 6,171 51.623 71,200 71 2.806 7,109 1,476,220 253,292 35,000 13,000 49,376,288 22,065,484 486,875 32,409,855 1
12 Los Lagos 36.415 27.36 24 22.644 47.298 2 4.186 2 27 20.928 8 25.728 2 2 162.58 86 6 - 0.323 0.201 104.641 0 0.374 19,901,362 0.426 85,949 8,729 2,023 192 1,409 3.5 11.8 23.719 2 8 2,160 994.622 122,157 2,251 4.186 4,708 7,108,761 264,511 96,915 63,500 101,170,739 40,816,121 737,875 62,581,190 2
13 Aysén 76.509 40.987 15 27.543 43.72 0 10.93 0 57 67.765 4 24.264 1 - 170.81 20 0 - 0.351 0.256 174.879 10.93 - 15,972,532 0.445 14,119 3,349 422 - 469 4.4 5.2 32.79 1 7 1,953 106.083 13,667 379 10.93 5,660 200,654 65,000 454,500 536,488 32,558,406 13,692,259 794,614 29,462,726 2
14 Magallanes y Antártica 59.008 59.34 12 34.676 71.606 0 13.26 1 15 137.244 0 18.366 1 2 297.25 21 3 - 0.283 0.18 53.041 6.63 0.367 20,075,842 0.414 8,659 2,527 660 - 216 5.4 6.1 86.192 1 22 616 5.115 322,800 397 13.26 4,217 558,678 266,000 525,000 32,100 30,955,753 18,036,736 357,752 30,288,105 1
In [55]:
chile_data_2.shape
Out[55]:
(15, 51)
In [56]:
chile_data_2 = chile_data_2.rename(columns={'VARIABLE': 'Region'})
In [57]:
chile_data_2
Out[57]:
Region Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Number of Winter Centers Number of shopping centers Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Number of Vineyards and Wine Routes Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) FNDR resources allocated to promotion (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
0 Arica y Parinacota 35.857 19.563 12 21.619 43.766 0 5.273 1 20 20.038 5 0 - - 240.55 17 6 - 0.318 0.26 42.184 15.819 0.513 11,884,613 0.426 26,467 2,387 981 319 265 7.3 8.2 84.369 1 11 253 94.915 45,560 137 5.273 6,517 1,060,490 193,364 118,413 92,641 36,370,835 11,757,543 446,922 24,729,573 1
1 Tarapacá 25.947 26.616 2 18.958 60.264 0 4.185 3 9 22.18 5 40.134 - 2 213.07 28 0 - 0.421 0.468 87.884 0 0.516 14,740,693 0.398 76,896 1,862 1,105 593 592 4.8 13.4 485.457 6 14.9 59,745 138.104 83,265 3,236 4.185 7,600 1,750,481 74,404 13,600 - 42,364,100 12,796,789 355,208 29,714,382 2
2 Antofagasta 37.248 35.446 22 29.96 69.233 3 4.049 3 36 20.446 2 14.292 - 3 259.23 39 2 - 0.546 0.314 109.315 0 0.381 22,383,612 0.495 123,017 4,228 2,324 7 765 6.2 7.3 12.146 13 9.6 30,220 178.143 238,010 16,666 4.049 16,280 2,846,340 200,000 149,000 149,744 38,262,875 29,066,423 192,572 50,841,622 1
3 Atacama 44.823 26.933 19 25.478 56.225 1 3.932 1 9 18.479 3 32.437 - - 184.01 35 1 - 0.39 0.197 66.841 0 0.488 17,068,077 0.533 21,705 3,125 965 14 504 6.4 10.2 15.727 3 29.3 17,860 145.477 53,046 3,456 3.932 11,614 556,668 94,100 187,035 - 35,948,639 16,986,593 233,225 30,224,699 2
4 Coquimbo 37.632 25.165 9 17.805 47.745 3 3.316 3 13 6.963 3 14.091 - 1 150.06 72 3 - 0.288 0.218 155.833 0 0.396 20,225,710 0.449 69,974 4,203 1,831 100 1,082 6 12.3 11.605 3 29 1,431 112.73 275,447 2,229 1.658 11,056 5,364,222 189,900 72,917 - 64,917,630 33,222,779 673,800 44,790,979 3
5 Valparaíso 52.408 47.05 16 27.756 55.85 9 1.948 12 44 12.209 3 18.71 2 8 230.65 179 3 5 0.311 0.291 87.671 1.948 0.432 24,137,492 0.445 236,177 19,176 6,765 166 2,761 7.5 11.6 50.654 10 10.7 16,641 143.52 1,557,887 4,287 1.299 5,649 3,058,423 129,481 63,996 1,079,295 101,802,434 24,239,428 811,772 51,647,041 1
6 Metropolitana 9.223 16.914 47 25.508 65.499 0 0 21 42 4.834 4 14.717 5 33 331.61 507 39 1 0.372 0.295 65.334 0.66 0.47 60,400,444 0.424 1,146,510 53,517 23,242 7,605 9,385 7.7 8.8 39.596 102 12.3 147,198 1295.113 7,307,884 6,196 0.33 19,352 13,709,951 56,982 - - 230,562,951 43,552,270 62,052 90,875,903 1
7 O'Higgins 18.959 49.422 6 19.946 43.427 0 2.562 3 39 9.992 2 14.95 1 2 117.3 114 3 7 0.203 0.22 51.241 0 0.396 18,182,163 0.477 111,020 4,894 2,451 762 1,136 6 9.9 11.529 7 10.2 26,831 95.805 265,601 3,056 1.281 4,700 754,000 179,000 6,000 345,000 50,200,910 17,850,727 444,491 43,045,390 1
8 Maule 15.637 19.943 10 23.852 35.899 9 1.101 1 24 8.7 5 25.383 - 3 109.73 130 0 2 0.245 0.227 27.53 1.101 0.427 25,936,948 0.53 124,820 6,018 4,771 194 1,875 6.4 15.8 11.012 5 10.7 1,371 48.346 165,417 1,311 1.101 7,183 647,510 168,850 8,000 218,000 97,730,159 19,292,650 486,500 48,826,193 2
9 Biobío 14.128 14.004 16 27.16 38.194 2 1.612 6 26 1.612 3 20.531 1 3 162.84 211 1 - 0.36 0.31 63.925 0 0.42 41,208,340 0.41 292,281 19,222 5,713 37 3,193 7.9 15.8 12.892 12 11.8 15,518 188.607 305,920 4,475 1.074 7,362 3,548,528 317,000 44,000 319,660 137,998,425 28,228,357 1,646,127 88,172,439 3
10 Araucanía 15.986 15.687 25 25.887 40.826 23 2.3 2 16 9.085 13 22.736 4 1 135.88 118 5 - 0.381 0.288 78.203 1.15 0.369 29,000,528 0.469 140,063 8,685 3,161 323 1,550 8 18.1 20.701 4 30.3 5,856 232.887 395,583 372 1.15 9,488 1,842,375 108,799 27,507 - 67,212,297 31,050,589 - 54,529,735 2
11 Los Ríos 34.512 26.207 9 30.472 42.649 2 2.806 1 40 8.698 12 0 1 - 191.32 44 1 - 0.274 0.27 101.011 0 0.298 15,834,434 0.44 22,076 3,235 818 29 773 6.4 14.3 11.223 4 11.3 6,171 51.623 71,200 71 2.806 7,109 1,476,220 253,292 35,000 13,000 49,376,288 22,065,484 486,875 32,409,855 1
12 Los Lagos 36.415 27.36 24 22.644 47.298 2 4.186 2 27 20.928 8 25.728 2 2 162.58 86 6 - 0.323 0.201 104.641 0 0.374 19,901,362 0.426 85,949 8,729 2,023 192 1,409 3.5 11.8 23.719 2 8 2,160 994.622 122,157 2,251 4.186 4,708 7,108,761 264,511 96,915 63,500 101,170,739 40,816,121 737,875 62,581,190 2
13 Aysén 76.509 40.987 15 27.543 43.72 0 10.93 0 57 67.765 4 24.264 1 - 170.81 20 0 - 0.351 0.256 174.879 10.93 - 15,972,532 0.445 14,119 3,349 422 - 469 4.4 5.2 32.79 1 7 1,953 106.083 13,667 379 10.93 5,660 200,654 65,000 454,500 536,488 32,558,406 13,692,259 794,614 29,462,726 2
14 Magallanes y Antártica 59.008 59.34 12 34.676 71.606 0 13.26 1 15 137.244 0 18.366 1 2 297.25 21 3 - 0.283 0.18 53.041 6.63 0.367 20,075,842 0.414 8,659 2,527 660 - 216 5.4 6.1 86.192 1 22 616 5.115 322,800 397 13.26 4,217 558,678 266,000 525,000 32,100 30,955,753 18,036,736 357,752 30,288,105 1
In [58]:
chile_data_2.loc[:, chile_data_2.dtypes == np.object]
Out[58]:
Region Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Number of Winter Centers Number of shopping centers Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Number of Vineyards and Wine Routes Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) FNDR resources allocated to promotion (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
0 Arica y Parinacota 35.857 19.563 12 21.619 43.766 0 5.273 1 20 20.038 5 0 - - 240.55 17 6 - 0.318 0.26 42.184 15.819 0.513 11,884,613 0.426 26,467 2,387 981 319 265 7.3 8.2 84.369 1 11 253 94.915 45,560 137 5.273 6,517 1,060,490 193,364 118,413 92,641 36,370,835 11,757,543 446,922 24,729,573 1
1 Tarapacá 25.947 26.616 2 18.958 60.264 0 4.185 3 9 22.18 5 40.134 - 2 213.07 28 0 - 0.421 0.468 87.884 0 0.516 14,740,693 0.398 76,896 1,862 1,105 593 592 4.8 13.4 485.457 6 14.9 59,745 138.104 83,265 3,236 4.185 7,600 1,750,481 74,404 13,600 - 42,364,100 12,796,789 355,208 29,714,382 2
2 Antofagasta 37.248 35.446 22 29.96 69.233 3 4.049 3 36 20.446 2 14.292 - 3 259.23 39 2 - 0.546 0.314 109.315 0 0.381 22,383,612 0.495 123,017 4,228 2,324 7 765 6.2 7.3 12.146 13 9.6 30,220 178.143 238,010 16,666 4.049 16,280 2,846,340 200,000 149,000 149,744 38,262,875 29,066,423 192,572 50,841,622 1
3 Atacama 44.823 26.933 19 25.478 56.225 1 3.932 1 9 18.479 3 32.437 - - 184.01 35 1 - 0.39 0.197 66.841 0 0.488 17,068,077 0.533 21,705 3,125 965 14 504 6.4 10.2 15.727 3 29.3 17,860 145.477 53,046 3,456 3.932 11,614 556,668 94,100 187,035 - 35,948,639 16,986,593 233,225 30,224,699 2
4 Coquimbo 37.632 25.165 9 17.805 47.745 3 3.316 3 13 6.963 3 14.091 - 1 150.06 72 3 - 0.288 0.218 155.833 0 0.396 20,225,710 0.449 69,974 4,203 1,831 100 1,082 6 12.3 11.605 3 29 1,431 112.73 275,447 2,229 1.658 11,056 5,364,222 189,900 72,917 - 64,917,630 33,222,779 673,800 44,790,979 3
5 Valparaíso 52.408 47.05 16 27.756 55.85 9 1.948 12 44 12.209 3 18.71 2 8 230.65 179 3 5 0.311 0.291 87.671 1.948 0.432 24,137,492 0.445 236,177 19,176 6,765 166 2,761 7.5 11.6 50.654 10 10.7 16,641 143.52 1,557,887 4,287 1.299 5,649 3,058,423 129,481 63,996 1,079,295 101,802,434 24,239,428 811,772 51,647,041 1
6 Metropolitana 9.223 16.914 47 25.508 65.499 0 0 21 42 4.834 4 14.717 5 33 331.61 507 39 1 0.372 0.295 65.334 0.66 0.47 60,400,444 0.424 1,146,510 53,517 23,242 7,605 9,385 7.7 8.8 39.596 102 12.3 147,198 1295.113 7,307,884 6,196 0.33 19,352 13,709,951 56,982 - - 230,562,951 43,552,270 62,052 90,875,903 1
7 O'Higgins 18.959 49.422 6 19.946 43.427 0 2.562 3 39 9.992 2 14.95 1 2 117.3 114 3 7 0.203 0.22 51.241 0 0.396 18,182,163 0.477 111,020 4,894 2,451 762 1,136 6 9.9 11.529 7 10.2 26,831 95.805 265,601 3,056 1.281 4,700 754,000 179,000 6,000 345,000 50,200,910 17,850,727 444,491 43,045,390 1
8 Maule 15.637 19.943 10 23.852 35.899 9 1.101 1 24 8.7 5 25.383 - 3 109.73 130 0 2 0.245 0.227 27.53 1.101 0.427 25,936,948 0.53 124,820 6,018 4,771 194 1,875 6.4 15.8 11.012 5 10.7 1,371 48.346 165,417 1,311 1.101 7,183 647,510 168,850 8,000 218,000 97,730,159 19,292,650 486,500 48,826,193 2
9 Biobío 14.128 14.004 16 27.16 38.194 2 1.612 6 26 1.612 3 20.531 1 3 162.84 211 1 - 0.36 0.31 63.925 0 0.42 41,208,340 0.41 292,281 19,222 5,713 37 3,193 7.9 15.8 12.892 12 11.8 15,518 188.607 305,920 4,475 1.074 7,362 3,548,528 317,000 44,000 319,660 137,998,425 28,228,357 1,646,127 88,172,439 3
10 Araucanía 15.986 15.687 25 25.887 40.826 23 2.3 2 16 9.085 13 22.736 4 1 135.88 118 5 - 0.381 0.288 78.203 1.15 0.369 29,000,528 0.469 140,063 8,685 3,161 323 1,550 8 18.1 20.701 4 30.3 5,856 232.887 395,583 372 1.15 9,488 1,842,375 108,799 27,507 - 67,212,297 31,050,589 - 54,529,735 2
11 Los Ríos 34.512 26.207 9 30.472 42.649 2 2.806 1 40 8.698 12 0 1 - 191.32 44 1 - 0.274 0.27 101.011 0 0.298 15,834,434 0.44 22,076 3,235 818 29 773 6.4 14.3 11.223 4 11.3 6,171 51.623 71,200 71 2.806 7,109 1,476,220 253,292 35,000 13,000 49,376,288 22,065,484 486,875 32,409,855 1
12 Los Lagos 36.415 27.36 24 22.644 47.298 2 4.186 2 27 20.928 8 25.728 2 2 162.58 86 6 - 0.323 0.201 104.641 0 0.374 19,901,362 0.426 85,949 8,729 2,023 192 1,409 3.5 11.8 23.719 2 8 2,160 994.622 122,157 2,251 4.186 4,708 7,108,761 264,511 96,915 63,500 101,170,739 40,816,121 737,875 62,581,190 2
13 Aysén 76.509 40.987 15 27.543 43.72 0 10.93 0 57 67.765 4 24.264 1 - 170.81 20 0 - 0.351 0.256 174.879 10.93 - 15,972,532 0.445 14,119 3,349 422 - 469 4.4 5.2 32.79 1 7 1,953 106.083 13,667 379 10.93 5,660 200,654 65,000 454,500 536,488 32,558,406 13,692,259 794,614 29,462,726 2
14 Magallanes y Antártica 59.008 59.34 12 34.676 71.606 0 13.26 1 15 137.244 0 18.366 1 2 297.25 21 3 - 0.283 0.18 53.041 6.63 0.367 20,075,842 0.414 8,659 2,527 660 - 216 5.4 6.1 86.192 1 22 616 5.115 322,800 397 13.26 4,217 558,678 266,000 525,000 32,100 30,955,753 18,036,736 357,752 30,288,105 1
In [59]:
# Remove $ symbol
chile_data_2 = chile_data_2.replace(r'[<$]', '', regex = True)
# Remove commas from numbers
chile_data_2 = chile_data_2.replace(',','', regex = True)
# Remove `-` character
chile_data_2 = chile_data_2.replace('-','', regex = True)
# Replace empty values with NaNs
chile_data_2 = chile_data_2.replace(r'^\s*$', np.nan, regex = True)
# Check NaNs in the dataset again
chile_data_2
Out[59]:
Region Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Number of Winter Centers Number of shopping centers Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Number of Vineyards and Wine Routes Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) FNDR resources allocated to promotion (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
0 Arica y Parinacota 35.857 19.563 12 21.619 43.766 0 5.273 1 20 20.038 5 0 NaN NaN 240.55 17 6 NaN 0.318 0.26 42.184 15.819 0.513 11884613 0.426 26467 2387 981 319 265 7.3 8.2 84.369 1 11 253 94.915 45560 137 5.273 6517 1060490 193364 118413 92641 36370835 11757543 446922 24729573 1
1 Tarapacá 25.947 26.616 2 18.958 60.264 0 4.185 3 9 22.18 5 40.134 NaN 2 213.07 28 0 NaN 0.421 0.468 87.884 0 0.516 14740693 0.398 76896 1862 1105 593 592 4.8 13.4 485.457 6 14.9 59745 138.104 83265 3236 4.185 7600 1750481 74404 13600 NaN 42364100 12796789 355208 29714382 2
2 Antofagasta 37.248 35.446 22 29.96 69.233 3 4.049 3 36 20.446 2 14.292 NaN 3 259.23 39 2 NaN 0.546 0.314 109.315 0 0.381 22383612 0.495 123017 4228 2324 7 765 6.2 7.3 12.146 13 9.6 30220 178.143 238010 16666 4.049 16280 2846340 200000 149000 149744 38262875 29066423 192572 50841622 1
3 Atacama 44.823 26.933 19 25.478 56.225 1 3.932 1 9 18.479 3 32.437 NaN NaN 184.01 35 1 NaN 0.39 0.197 66.841 0 0.488 17068077 0.533 21705 3125 965 14 504 6.4 10.2 15.727 3 29.3 17860 145.477 53046 3456 3.932 11614 556668 94100 187035 NaN 35948639 16986593 233225 30224699 2
4 Coquimbo 37.632 25.165 9 17.805 47.745 3 3.316 3 13 6.963 3 14.091 NaN 1 150.06 72 3 NaN 0.288 0.218 155.833 0 0.396 20225710 0.449 69974 4203 1831 100 1082 6 12.3 11.605 3 29 1431 112.73 275447 2229 1.658 11056 5364222 189900 72917 NaN 64917630 33222779 673800 44790979 3
5 Valparaíso 52.408 47.05 16 27.756 55.85 9 1.948 12 44 12.209 3 18.71 2 8 230.65 179 3 5 0.311 0.291 87.671 1.948 0.432 24137492 0.445 236177 19176 6765 166 2761 7.5 11.6 50.654 10 10.7 16641 143.52 1557887 4287 1.299 5649 3058423 129481 63996 1079295 101802434 24239428 811772 51647041 1
6 Metropolitana 9.223 16.914 47 25.508 65.499 0 0 21 42 4.834 4 14.717 5 33 331.61 507 39 1 0.372 0.295 65.334 0.66 0.47 60400444 0.424 1146510 53517 23242 7605 9385 7.7 8.8 39.596 102 12.3 147198 1295.113 7307884 6196 0.33 19352 13709951 56982 NaN NaN 230562951 43552270 62052 90875903 1
7 O'Higgins 18.959 49.422 6 19.946 43.427 0 2.562 3 39 9.992 2 14.95 1 2 117.3 114 3 7 0.203 0.22 51.241 0 0.396 18182163 0.477 111020 4894 2451 762 1136 6 9.9 11.529 7 10.2 26831 95.805 265601 3056 1.281 4700 754000 179000 6000 345000 50200910 17850727 444491 43045390 1
8 Maule 15.637 19.943 10 23.852 35.899 9 1.101 1 24 8.7 5 25.383 NaN 3 109.73 130 0 2 0.245 0.227 27.53 1.101 0.427 25936948 0.53 124820 6018 4771 194 1875 6.4 15.8 11.012 5 10.7 1371 48.346 165417 1311 1.101 7183 647510 168850 8000 218000 97730159 19292650 486500 48826193 2
9 Biobío 14.128 14.004 16 27.16 38.194 2 1.612 6 26 1.612 3 20.531 1 3 162.84 211 1 NaN 0.36 0.31 63.925 0 0.42 41208340 0.41 292281 19222 5713 37 3193 7.9 15.8 12.892 12 11.8 15518 188.607 305920 4475 1.074 7362 3548528 317000 44000 319660 137998425 28228357 1646127 88172439 3
10 Araucanía 15.986 15.687 25 25.887 40.826 23 2.3 2 16 9.085 13 22.736 4 1 135.88 118 5 NaN 0.381 0.288 78.203 1.15 0.369 29000528 0.469 140063 8685 3161 323 1550 8 18.1 20.701 4 30.3 5856 232.887 395583 372 1.15 9488 1842375 108799 27507 NaN 67212297 31050589 NaN 54529735 2
11 Los Ríos 34.512 26.207 9 30.472 42.649 2 2.806 1 40 8.698 12 0 1 NaN 191.32 44 1 NaN 0.274 0.27 101.011 0 0.298 15834434 0.44 22076 3235 818 29 773 6.4 14.3 11.223 4 11.3 6171 51.623 71200 71 2.806 7109 1476220 253292 35000 13000 49376288 22065484 486875 32409855 1
12 Los Lagos 36.415 27.36 24 22.644 47.298 2 4.186 2 27 20.928 8 25.728 2 2 162.58 86 6 NaN 0.323 0.201 104.641 0 0.374 19901362 0.426 85949 8729 2023 192 1409 3.5 11.8 23.719 2 8 2160 994.622 122157 2251 4.186 4708 7108761 264511 96915 63500 101170739 40816121 737875 62581190 2
13 Aysén 76.509 40.987 15 27.543 43.72 0 10.93 0 57 67.765 4 24.264 1 NaN 170.81 20 0 NaN 0.351 0.256 174.879 10.93 NaN 15972532 0.445 14119 3349 422 NaN 469 4.4 5.2 32.79 1 7 1953 106.083 13667 379 10.93 5660 200654 65000 454500 536488 32558406 13692259 794614 29462726 2
14 Magallanes y Antártica 59.008 59.34 12 34.676 71.606 0 13.26 1 15 137.244 0 18.366 1 2 297.25 21 3 NaN 0.283 0.18 53.041 6.63 0.367 20075842 0.414 8659 2527 660 NaN 216 5.4 6.1 86.192 1 22 616 5.115 322800 397 13.26 4217 558678 266000 525000 32100 30955753 18036736 357752 30288105 1
In [60]:
chile_data_2.isnull().sum(axis = 0)
#checking for NaN/ Null
Out[60]:
Region                                                                                                                          0
Density of restaurants and other food services per 100,000 inhabitants                                                          0
Density of People employed in restaurants and the like per 10,000 inhabitants                                                   0
Car rental companies                                                                                                            0
Densidad de camas en hospitales por cada 10.000 habitantes                                                                      0
Density of beds in hospitals per 10,000 inhabitants                                                                             0
Number of spas                                                                                                                  0
Density of gambling casinos per million inhabitants                                                                             0
Number of golf courses                                                                                                          0
Number of craft centers                                                                                                         0
Density of tour guides per 100,000 inhabitants                                                                                  0
Number of thermal centers                                                                                                       0
Density of Sports Facilities and Venues per 10,000 inhabitants                                                                  0
Number of Winter Centers                                                                                                        6
Number of shopping centers                                                                                                      4
Penetration of telephone lines in service per 100 inhabitants                                                                   0
Density of service stations                                                                                                     0
Number of tour-operator companies certified with the tourism quality seal                                                       0
Number of Vineyards and Wine Routes                                                                                            11
Perception of exposure to crime (%)                                                                                             0
Percentage of victimized households with at least one victim                                                                    0
Density of homicides per million inhabitants                                                                                    0
Density of crimes against public health per million inhabitants                                                                 0
Black figure index                                                                                                              1
Budget for public safety (Thousands of $)                                                                                       0
Percentage of households that reported at least one crime                                                                       0
Number of declared crimes                                                                                                       0
Number of crimes investigated                                                                                                   0
Number of accidents (roads, air and waterways)                                                                                  0
Illegal commerce                                                                                                                2
Number of Carabineros                                                                                                           0
Unemployment rate                                                                                                               0
Poverty rate                                                                                                                    0
Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants     0
Number of strikes carried out                                                                                                   0
Average (days) duration of a strike                                                                                             0
Person-day cost of a strike                                                                                                     0
Density of Bank Branches per million inhabitants                                                                                0
Floating population                                                                                                             0
Volume of exports                                                                                                               0
Density of Tourist Information Offices per million inhabitants                                                                  0
Number of visits to Tourist Information Offices                                                                                 0
Average monthly global searches by tourist attraction on the internet                                                           0
National tourism promotion budget (Thousands of USD)                                                                            0
International tourism promotion budget (Thousands of USD)                                                                       1
FNDR resources allocated to promotion (Thousands of USD)                                                                        5
Investments in public infrastructure made by the Ministry of Public Works                                                       0
Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos)                        0
Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population                                       1
Funds obtained from FNRD (Thousands of pesos)                                                                                   0
Number of regional strategic development plans                                                                                  0
dtype: int64

we have NaNs in 8 columns:

  • Number of Winter Centers (6 NaNs)
  • Number of shopping centers (4 NaNs)
  • Number of Vineyards and Wine Routes (11 NaNs)
  • Black figure index (1 NaN)
  • Illegal commerce (2 NaNs)
  • International tourism promotion budget (Thousands of USD) (1 NaN)
  • FNDR resources allocated to promotion (Thousands of USD) (5 NaNs)
  • Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population (1 NaN)

In our case we must drop some variables:

  • Number of Winter Centers (6 NaNs)
  • Number of shopping centers (4 NaNs)
  • Number of Vineyards and Wine Routes (11 NaNs)
  • FNDR resources allocated to promotion (Thousands of USD) (5 NaNs)

These are variables where 30-70 percent of data is missing. It does not makes sense to impute empty data here plus in general the imputation is a bad idea here, we have only 15 rows here and each missing data point in a row can skew our results.

In [61]:
# Drop 4 columns with many NaNs
chile_data_2 = chile_data_2 = chile_data_2.drop(['Number of Winter Centers', 
                                                 'Number of shopping centers',
                                                 'Number of Vineyards and Wine Routes',
                                                 'FNDR resources allocated to promotion (Thousands of USD)'], axis = 1)
In [62]:
chile_data_2.shape
Out[62]:
(15, 47)
In [63]:
# Impute data in four columns
imputer = SimpleImputer(missing_values = np.nan, strategy = 'mean')
chile_data_2[['Illegal commerce',
              'Black figure index',
              'International tourism promotion budget (Thousands of USD)',
              'Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population']] = imputer.fit_transform(chile_data_2[['Illegal commerce',
              'Black figure index',                                                                                                                          
              'International tourism promotion budget (Thousands of USD)',
              'Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population']])
In [64]:
# Check NaNs in the dataset
chile_data_2.isnull().sum(axis = 0)
Out[64]:
Region                                                                                                                         0
Density of restaurants and other food services per 100,000 inhabitants                                                         0
Density of People employed in restaurants and the like per 10,000 inhabitants                                                  0
Car rental companies                                                                                                           0
Densidad de camas en hospitales por cada 10.000 habitantes                                                                     0
Density of beds in hospitals per 10,000 inhabitants                                                                            0
Number of spas                                                                                                                 0
Density of gambling casinos per million inhabitants                                                                            0
Number of golf courses                                                                                                         0
Number of craft centers                                                                                                        0
Density of tour guides per 100,000 inhabitants                                                                                 0
Number of thermal centers                                                                                                      0
Density of Sports Facilities and Venues per 10,000 inhabitants                                                                 0
Penetration of telephone lines in service per 100 inhabitants                                                                  0
Density of service stations                                                                                                    0
Number of tour-operator companies certified with the tourism quality seal                                                      0
Perception of exposure to crime (%)                                                                                            0
Percentage of victimized households with at least one victim                                                                   0
Density of homicides per million inhabitants                                                                                   0
Density of crimes against public health per million inhabitants                                                                0
Black figure index                                                                                                             0
Budget for public safety (Thousands of $)                                                                                      0
Percentage of households that reported at least one crime                                                                      0
Number of declared crimes                                                                                                      0
Number of crimes investigated                                                                                                  0
Number of accidents (roads, air and waterways)                                                                                 0
Illegal commerce                                                                                                               0
Number of Carabineros                                                                                                          0
Unemployment rate                                                                                                              0
Poverty rate                                                                                                                   0
Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants    0
Number of strikes carried out                                                                                                  0
Average (days) duration of a strike                                                                                            0
Person-day cost of a strike                                                                                                    0
Density of Bank Branches per million inhabitants                                                                               0
Floating population                                                                                                            0
Volume of exports                                                                                                              0
Density of Tourist Information Offices per million inhabitants                                                                 0
Number of visits to Tourist Information Offices                                                                                0
Average monthly global searches by tourist attraction on the internet                                                          0
National tourism promotion budget (Thousands of USD)                                                                           0
International tourism promotion budget (Thousands of USD)                                                                      0
Investments in public infrastructure made by the Ministry of Public Works                                                      0
Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos)                       0
Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population                                      0
Funds obtained from FNRD (Thousands of pesos)                                                                                  0
Number of regional strategic development plans                                                                                 0
dtype: int64
In [65]:
# Converting into numeric
chile_data_2 = chile_data_2.set_index('Region')

# Select columns
cols = chile_data_2.loc[:, chile_data_2.dtypes == np.object].columns

# Convert to numeric
chile_data_2[cols] = chile_data_2[cols].apply(pd.to_numeric, errors = 'coerce', axis = 1)

# Now all our columns are integers or floats
chile_data_2 = chile_data_2.reset_index()
In [66]:
chile_data_2.dtypes
Out[66]:
Region                                                                                                                          object
Density of restaurants and other food services per 100,000 inhabitants                                                         float64
Density of People employed in restaurants and the like per 10,000 inhabitants                                                  float64
Car rental companies                                                                                                           float64
Densidad de camas en hospitales por cada 10.000 habitantes                                                                     float64
Density of beds in hospitals per 10,000 inhabitants                                                                            float64
Number of spas                                                                                                                 float64
Density of gambling casinos per million inhabitants                                                                            float64
Number of golf courses                                                                                                         float64
Number of craft centers                                                                                                        float64
Density of tour guides per 100,000 inhabitants                                                                                 float64
Number of thermal centers                                                                                                      float64
Density of Sports Facilities and Venues per 10,000 inhabitants                                                                 float64
Penetration of telephone lines in service per 100 inhabitants                                                                  float64
Density of service stations                                                                                                    float64
Number of tour-operator companies certified with the tourism quality seal                                                      float64
Perception of exposure to crime (%)                                                                                            float64
Percentage of victimized households with at least one victim                                                                   float64
Density of homicides per million inhabitants                                                                                   float64
Density of crimes against public health per million inhabitants                                                                float64
Black figure index                                                                                                             float64
Budget for public safety (Thousands of $)                                                                                      float64
Percentage of households that reported at least one crime                                                                      float64
Number of declared crimes                                                                                                      float64
Number of crimes investigated                                                                                                  float64
Number of accidents (roads, air and waterways)                                                                                 float64
Illegal commerce                                                                                                               float64
Number of Carabineros                                                                                                          float64
Unemployment rate                                                                                                              float64
Poverty rate                                                                                                                   float64
Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants    float64
Number of strikes carried out                                                                                                  float64
Average (days) duration of a strike                                                                                            float64
Person-day cost of a strike                                                                                                    float64
Density of Bank Branches per million inhabitants                                                                               float64
Floating population                                                                                                            float64
Volume of exports                                                                                                              float64
Density of Tourist Information Offices per million inhabitants                                                                 float64
Number of visits to Tourist Information Offices                                                                                float64
Average monthly global searches by tourist attraction on the internet                                                          float64
National tourism promotion budget (Thousands of USD)                                                                           float64
International tourism promotion budget (Thousands of USD)                                                                      float64
Investments in public infrastructure made by the Ministry of Public Works                                                      float64
Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos)                       float64
Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population                                      float64
Funds obtained from FNRD (Thousands of pesos)                                                                                  float64
Number of regional strategic development plans                                                                                 float64
dtype: object

2. Principal Component Analysis

In [67]:
# Finally, we need to standardize data for applying PCA

# Create a copy
chile_data_s_2 = chile_data_2.copy()

# Standardize
scaler = StandardScaler()
chile_data_s_2.loc[:, chile_data_s_2.columns != 'Region'] = scaler.fit_transform(chile_data_s_2.loc[:, chile_data_s_2.columns != 'Region'])

# Set region as an index column
chile_data_s_2 = chile_data_s_2.set_index('Region')
pass
In [68]:
chile_data_s_2
Out[68]:
Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
Region
Arica y Parinacota 0.087073 -0.798010 -0.411459 -0.820845 -0.633616 -0.607010 0.342739 -0.560316 -0.554658 -0.135893 0.057354 -1.871499 0.695488 -0.749525 0.121472 -0.248797 -0.093065 -1.087756 2.859839 1.648551 -0.996415 -0.652770 -0.512578 -0.565722 -0.513953 -2.587452e-01 -0.663000 0.812831 -0.834653 0.204305 -0.433573 -0.533574 -0.597159 -0.448859 -0.392595 -0.771859 0.417721 -0.484381 -0.554876 0.288149 -6.791934e-02 -0.731979 -1.298074 -0.291691 -1.151570 -0.953463
Tarapacá -0.462238 -0.260925 -1.375815 -1.416782 0.849682 -0.607010 0.025557 -0.186772 -1.336868 -0.072248 0.057354 2.063212 0.255363 -0.658989 -0.521615 1.049823 2.963394 0.083237 -0.549390 1.700415 -0.757515 -1.353955 -0.328183 -0.606462 -0.491469 -1.099479e-01 -0.515181 -1.141089 0.586808 3.657754 -0.229057 -0.038898 1.017526 -0.328069 -0.371529 0.000349 0.115821 -0.228403 -0.353907 -1.225117 -7.595427e-01 -0.616910 -1.189453 -0.545976 -0.899207 0.476731
Antofagasta 0.164176 0.411478 0.552897 1.047141 1.656064 -0.101168 -0.014090 -0.186772 0.583102 -0.123770 -0.802955 -0.470321 0.994671 -0.568454 -0.307253 2.625818 0.700439 0.632374 -0.549390 -0.633488 -0.118212 1.075151 -0.159541 -0.422858 -0.270437 -4.281786e-01 -0.436977 -0.046894 -1.080675 -0.417550 0.057264 -0.711150 0.216182 -0.216088 -0.285070 3.346832 0.078083 1.823205 -0.034723 0.372564 1.339133e-01 -0.695652 0.511042 -0.996899 0.170390 -0.953463
Atacama 0.584058 -0.236785 0.263591 0.043387 0.486545 -0.438396 -0.048199 -0.560316 -1.336868 -0.182215 -0.516185 1.308603 -0.210069 -0.601376 -0.414434 0.658976 -1.018820 -0.455958 -0.549390 1.216346 -0.562837 2.026759 -0.529991 -0.508452 -0.516855 -4.243772e-01 -0.554961 0.109420 -0.287937 -0.386717 -0.351766 1.787599 -0.119284 -0.307448 -0.388412 0.055168 0.045618 0.720348 -0.701621 -0.974568 3.848926e-01 -0.740085 -0.751536 -0.884185 -0.873371 0.476731
Coquimbo 0.185461 -0.371418 -0.700765 -1.674999 -0.275873 -0.101168 -0.227780 -0.186772 -1.052428 -0.524389 -0.516185 -0.490027 -0.753819 -0.296847 -0.200071 -0.627036 -0.710235 1.824327 -0.549390 -0.374165 -0.298713 -0.076796 -0.353494 -0.424798 -0.359829 -3.776744e-01 -0.293679 -0.203208 0.286115 -0.422208 -0.351766 1.749547 -0.565187 -0.399034 -0.264153 -0.250575 -0.585376 0.588459 0.698645 0.244084 -3.681311e-01 -0.183891 0.945462 0.337347 -0.135932 1.906925
Valparaíso 1.004495 1.295121 -0.025716 0.553550 0.452829 0.910515 -0.626589 1.494175 1.151982 -0.368515 -0.516185 -0.037183 0.536928 0.583817 -0.200071 -0.337053 0.362465 0.077779 -0.129568 0.248209 0.028493 -0.176966 0.254231 0.737122 0.534818 -3.418327e-01 0.465305 0.969144 0.094764 -0.085988 -0.065445 -0.571626 -0.152369 -0.312921 0.452371 0.262237 -0.684992 -0.689542 0.027050 -0.524494 -4.269976e-01 0.524283 0.006527 0.719887 0.211166 -0.953463
Metropolitana -1.389249 -0.999731 2.963788 0.050106 1.320349 -0.607010 -1.194483 3.175121 1.009762 -0.587648 -0.229416 -0.428654 2.153925 3.283423 3.658450 0.432033 0.421243 -0.494572 -0.407151 0.905159 3.061756 -0.702855 3.582885 3.402018 3.522474 3.697959e+00 3.459654 1.125458 -0.670638 -0.181200 3.697638 -0.368682 3.391107 2.907840 3.665005 0.737921 -0.953872 2.549305 3.129454 -1.446739 -9.602287e-17 2.996435 2.025096 -1.358777 2.197183 -0.953463
O'Higgins -0.849583 1.475749 -0.990072 -1.195517 -0.664094 -0.607010 -0.447591 -0.186772 0.796432 -0.434389 -0.802955 -0.405811 -1.278511 0.048834 -0.200071 -1.698713 -0.680846 -0.855684 -0.549390 -0.374165 -0.469648 0.624389 -0.203408 -0.371176 -0.247409 -1.817148e-02 -0.269268 -0.203208 -0.369944 -0.422863 -0.188154 -0.635046 0.124200 -0.446370 -0.269654 -0.044503 -0.689987 -0.913848 -0.644146 0.105427 -8.096924e-01 -0.466447 -0.661217 -0.298431 -0.224305 -0.953463
Maule -1.033721 -0.769073 -0.604330 -0.320759 -1.340920 0.910515 -0.873512 -0.560316 -0.270218 -0.472778 0.057354 0.617034 -1.399753 0.180522 -0.521615 -1.169178 -0.577984 -1.463242 -0.312109 0.161768 0.179011 1.951632 -0.152948 -0.283952 0.173260 -3.266272e-01 0.064793 0.109420 1.242867 -0.427314 -0.269960 -0.571626 -0.566815 -0.579103 -0.325629 -0.479322 -0.739933 -0.326965 -0.675162 -0.023689 -7.964951e-01 0.446097 -0.510508 -0.181958 0.068356 0.476731
Biobío -1.117365 -1.221327 -0.025716 0.420075 -1.134581 -0.269782 -0.724542 0.373544 -0.127998 -0.683383 -0.516185 0.141347 -0.549132 0.847193 -0.414434 0.280737 0.641661 -0.530676 -0.549390 0.040751 1.456407 -1.053447 0.459377 0.740691 0.344066 -4.118869e-01 0.660589 1.281772 1.242867 -0.411127 0.016361 -0.432102 -0.182849 -0.186823 -0.247127 0.309082 -0.747425 -0.284657 0.169799 1.860898 -5.589440e-01 1.219232 0.423448 3.033208 2.060316 1.906925
Araucanía -1.014376 -1.093167 0.842204 0.134984 -0.897944 3.271109 -0.523971 -0.373544 -0.839098 -0.461338 2.351511 0.357523 -0.980929 0.081756 0.014291 0.545505 0.318381 -0.164824 -0.301548 -0.840946 0.435268 0.424050 -0.097212 -0.076991 -0.118670 -2.565730e-01 -0.082122 1.359928 1.871590 -0.343890 -0.310863 1.914439 -0.445087 -0.062981 -0.197030 -0.713302 -0.726337 0.217846 -0.327141 -0.787585 -6.677754e-01 -0.139835 0.718426 0.000000 0.357106 0.476731
Los Ríos 0.012520 -0.292070 -0.700765 1.161805 -0.734043 -0.269782 -0.376458 -0.560316 0.867542 -0.472837 2.064742 -1.871499 -0.092990 -0.527301 -0.414434 -0.803547 0.053880 0.419596 -0.549390 -2.068406 -0.666027 -0.302177 -0.528634 -0.499916 -0.543509 -4.162314e-01 -0.433361 0.109420 0.832830 -0.425497 -0.310863 -0.495522 -0.436538 -0.569938 -0.378269 -0.788305 -0.266827 -0.344456 -0.433789 1.050481 -6.183318e-01 -0.482279 -0.220693 -0.180918 -0.762745 -0.953463
Los Lagos 0.118003 -0.204269 0.745769 -0.591294 -0.316062 -0.269782 0.025849 -0.373544 -0.056888 -0.109448 0.917663 0.650857 -0.553296 -0.181620 0.121472 -0.185757 -0.960042 0.512610 -0.549390 -0.754505 -0.325844 -0.652770 -0.295081 -0.073576 -0.325015 -3.277133e-01 -0.145860 -2.157128 0.149436 -0.317904 -0.392670 -0.914094 -0.545401 2.067430 -0.349799 -0.245093 0.116098 -0.911957 1.206767 1.193196 -2.097769e-01 0.512154 1.739115 0.515001 0.764723 0.476731
Aysén 2.340413 0.833425 -0.122152 0.505849 -0.637751 -0.607010 1.991907 -0.747087 2.076412 1.282215 -0.229416 0.507328 -0.421483 -0.724833 -0.521615 0.167266 -0.151843 2.312351 1.806187 0.000000 -0.654476 -0.176966 -0.557729 -0.491070 -0.615313 -6.173829e-17 -0.570783 -1.453717 -1.654726 -0.239801 -0.433573 -1.040934 -0.551019 -0.417625 -0.410414 -0.711558 1.987437 -0.686942 -0.805315 -1.344744 2.149798e+00 -0.805176 -1.095859 0.672315 -0.911947 0.476731
Magallanes y Antártica 1.370333 2.231003 -0.411459 2.103300 1.869415 -0.607010 2.671164 -0.560316 -0.910208 3.346638 -1.376494 -0.070909 1.603608 -0.716603 -0.200071 -0.690076 -1.268627 -0.809562 0.879473 -0.875522 -0.311249 -0.953278 -0.577694 -0.554858 -0.572158 -6.173829e-17 -0.685150 -0.672149 -1.408704 0.220001 -0.433573 0.861667 -0.587307 -0.700011 -0.237696 -0.707072 2.633969 -1.028010 -0.701036 1.212137 2.615002e+00 -0.835946 -0.641776 -0.538923 -0.870161 -0.953463

Eigenvalues and eigenvectors

In [69]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s_2.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)

# Print 
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors 
[[ 0.13313201+0.j         -0.23035129+0.j          0.0689068 +0.j
  ... -0.04247971+0.0370457j   0.04872969+0.04915378j
   0.04872969-0.04915378j]
 [ 0.09719407+0.j         -0.22771205+0.j          0.08783438+0.j
  ...  0.02191131-0.02586547j -0.02678085+0.00210744j
  -0.02678085-0.00210744j]
 [-0.19581156+0.j         -0.06937946+0.j          0.08290993+0.j
  ... -0.07879693+0.06185894j  0.02710413+0.04667135j
   0.02710413-0.04667135j]
 ...
 [ 0.03694777+0.j          0.14566667+0.j          0.20091417+0.j
  ...  0.10080102-0.00846084j -0.02357666-0.00280769j
  -0.02357666+0.00280769j]
 [-0.19303555+0.j          0.09406386+0.j          0.13395992+0.j
  ... -0.02083788+0.03723748j  0.10429246+0.02310865j
   0.10429246-0.02310865j]
 [ 0.02888236+0.j          0.19164569+0.j         -0.0470399 +0.j
  ...  0.00254517-0.02573687j -0.03570821-0.06621232j
  -0.03570821+0.06621232j]]

Eigenvalues 
[ 1.88351109e+01+0.00000000e+00j  8.20392035e+00+0.00000000e+00j
  4.31308598e+00+0.00000000e+00j  3.04535368e+00+0.00000000e+00j
  2.83788710e+00+0.00000000e+00j  2.45872307e+00+0.00000000e+00j
  2.11826332e+00+0.00000000e+00j  1.95116248e+00+0.00000000e+00j
  1.73847859e+00+0.00000000e+00j  1.52051249e+00+0.00000000e+00j
  1.03145621e+00+0.00000000e+00j  6.55262136e-01+0.00000000e+00j
  3.87450124e-01+0.00000000e+00j  1.89047837e-01+0.00000000e+00j
  9.80403397e-16+0.00000000e+00j  7.00757475e-16+1.58504386e-16j
  7.00757475e-16-1.58504386e-16j -8.77352586e-16+0.00000000e+00j
  5.14456544e-16+2.85945807e-16j  5.14456544e-16-2.85945807e-16j
  5.75263855e-16+0.00000000e+00j  4.29763873e-16+0.00000000e+00j
  2.95300025e-16+1.15018850e-16j  2.95300025e-16-1.15018850e-16j
  2.97378861e-16+5.66592080e-17j  2.97378861e-16-5.66592080e-17j
 -6.31925937e-16+0.00000000e+00j -4.86908356e-16+1.63779169e-16j
 -4.86908356e-16-1.63779169e-16j -5.24509357e-16+6.30086846e-17j
 -5.24509357e-16-6.30086846e-17j -5.07369230e-16+0.00000000e+00j
 -4.27735497e-16+0.00000000e+00j -3.41983903e-16+0.00000000e+00j
 -2.68174710e-16+5.21912258e-17j -2.68174710e-16-5.21912258e-17j
 -2.80060656e-16+0.00000000e+00j  1.22600042e-16+6.64874638e-17j
  1.22600042e-16-6.64874638e-17j -1.18016904e-16+4.88284602e-17j
 -1.18016904e-16-4.88284602e-17j  7.38069176e-17+0.00000000e+00j
 -9.15956110e-18+5.77505472e-17j -9.15956110e-18-5.77505472e-17j
 -2.99869362e-17+2.43347586e-17j -2.99869362e-17-2.43347586e-17j]
In [70]:
myPCA = PCA()


x = myPCA.fit(chile_data_s_2)
In [71]:
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
Out[71]:
Text(0.5, 1.0, 'All Principle Components')
In [72]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [73]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[73]:
array([0.38216167, 0.54861803, 0.63612992, 0.6979197 , 0.75550002,
       0.80538715, 0.84836641, 0.88795521, 0.92322869, 0.95407967,
       0.97500777, 0.98830294, 0.99616425, 1.        , 1.        ])

will use only first 7 components, which explain 83.6% of variance.

So, instead of 51 columns in our dataset we have 7 uncorrelated components,

Perform PCA with 7 components now and fit the dataset again

</span>

In [74]:
# Initialize PCA with 7 components
myPCA = PCA(n_components = 7)
In [75]:
pca_model = myPCA.fit(chile_data_s_2)


print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are 
 [[-1.33132006e-01 -9.71940725e-02  1.95811562e-01 -1.00633015e-02
   5.76552543e-02  6.68292211e-03 -1.39481743e-01  2.19424069e-01
   5.81024190e-02 -9.80069137e-02  1.89879021e-03 -1.70435214e-02
   1.04726340e-01  2.29038996e-01  2.17724253e-01  4.68836016e-02
   5.03568581e-02 -4.16694955e-02 -7.76542633e-02  4.70672529e-02
   2.18917075e-01 -3.48170710e-02  2.34362576e-01  2.30352223e-01
   2.32391896e-01  2.08386502e-01  2.33117564e-01  1.08427604e-01
   1.85849384e-02 -2.62713787e-02  2.27836728e-01 -2.71694372e-02
   2.06880041e-01  1.90524998e-01  2.24280863e-01  8.20218888e-02
  -1.26775062e-01  1.68544853e-01  2.12374047e-01 -6.44262692e-02
  -6.44486770e-02  2.20135638e-01  1.68277229e-01 -3.69477651e-02
   1.93035546e-01 -2.88823556e-02]
 [ 2.30351292e-01  2.27712050e-01  6.93794581e-02  1.71639585e-01
   2.63718306e-01 -1.95403178e-01  2.61322689e-01  5.34722980e-02
   1.03726706e-01  2.75752726e-01 -1.89730561e-01 -2.92279035e-02
   2.71310833e-01 -1.55981324e-02  9.44690831e-02  6.47004869e-02
  -4.57325361e-02  5.90127297e-02  1.71328123e-01  3.62242215e-02
  -1.54726567e-02 -9.55109858e-02  4.41763502e-02  3.57031601e-02
   3.54338216e-02  1.26485791e-01  1.41245990e-02 -1.18670298e-01
  -3.29769143e-01  4.00781412e-02  8.45844050e-02 -6.80922600e-02
   8.66982272e-02  4.07449335e-02  9.60635484e-02  5.82095023e-02
   2.77196673e-01  3.04942383e-02  2.51100716e-02 -5.88335533e-02
   3.03379869e-01 -4.85144078e-02 -6.30021675e-02 -1.45666668e-01
  -9.40638554e-02 -1.91645687e-01]
 [-6.89068023e-02 -8.78343802e-02 -8.29099311e-02 -2.21100162e-01
   1.75429953e-01 -4.73509563e-02 -5.03528122e-02 -6.02748486e-04
  -2.35358257e-01 -7.44154323e-02 -6.48955445e-02  2.77975454e-01
   6.80221109e-02 -7.19432839e-02 -1.81128180e-02  2.55891875e-01
   3.35197481e-01 -2.02583649e-02 -5.53894206e-02  3.45710584e-01
  -8.03622867e-02  1.24990764e-02 -8.39842957e-03 -6.14561946e-02
  -2.64249892e-02  1.65549222e-02 -4.84295692e-02 -5.03205763e-02
   1.12172842e-02  4.00850615e-01  1.81200330e-02  1.08326713e-01
   1.75037033e-01 -3.93184470e-02 -6.03556037e-03  1.19990187e-01
  -3.65338808e-02  1.45648520e-01 -3.68609241e-02 -2.82826872e-01
  -1.00615717e-01 -9.66729038e-02 -1.52173128e-01 -2.00914174e-01
  -1.33959921e-01  4.70399010e-02]
 [ 2.80784130e-02  2.36007442e-02  1.69679121e-01  2.31695981e-01
   2.07537609e-01  2.15617864e-01 -4.08766826e-02 -6.37984227e-02
  -6.16156229e-02 -1.85221201e-02  3.17105555e-02 -7.75838797e-03
   4.40360519e-02 -7.62541749e-02 -4.72012880e-02  3.08374270e-01
  -1.12828098e-01  7.57650951e-02 -2.24282233e-01 -2.21497910e-01
   6.08256244e-03  3.61261507e-01 -5.12265667e-02 -7.07351253e-02
  -5.07773488e-02 -9.00492965e-02 -7.05745807e-02  9.28359020e-02
  -7.85431609e-03 -2.36562438e-01 -2.34242869e-02  2.42081072e-01
  -4.86295837e-02 -4.42121739e-02 -4.23739891e-02  3.47946518e-01
  -4.50589513e-02  3.09601050e-01 -2.69981782e-02 -5.44818582e-03
   3.17126939e-02 -1.12859775e-01  1.63640953e-01 -2.12271012e-01
   5.99367880e-03 -6.06961277e-02]
 [ 1.22165134e-01 -4.58479447e-02  6.17916567e-02 -1.81180662e-02
   4.52048821e-02 -6.24925452e-02  1.11906018e-01 -2.41633497e-02
  -1.35535320e-02  6.24674818e-02 -7.34807079e-03  2.77051854e-01
  -5.78117653e-02 -2.45736508e-02 -6.64597691e-02  2.09205467e-01
   9.56615451e-02  3.95013932e-01 -1.74264053e-01 -1.12312703e-01
   6.00172642e-02 -2.30829399e-01 -2.21875638e-02  8.05279105e-03
  -5.52908133e-02 -6.09412759e-02 -6.10325525e-03 -3.05601935e-01
   3.08879941e-02  8.15239030e-02 -4.72735591e-02  2.74397902e-03
  -2.96249043e-02  1.71588575e-01 -6.60777459e-02  8.81473243e-02
   1.01913401e-01 -1.85250915e-03  1.75086272e-01  1.05300812e-01
   1.06364295e-01  7.22972346e-02  2.60058145e-01  2.87998375e-01
   1.93058929e-01  3.90019373e-01]
 [-1.41465715e-02  9.78615784e-02 -1.27031012e-01  2.12116393e-02
   1.78985656e-02 -2.03918118e-01 -1.53711499e-01  5.31112061e-02
   3.57861493e-01 -2.10819806e-01 -8.22258903e-02 -1.89785667e-01
   3.45587879e-02 -3.64121660e-02 -1.11489699e-01  1.49562685e-01
   2.59862784e-01  9.52931101e-02 -8.03868867e-02 -7.79103440e-02
  -7.99559958e-02 -4.53007549e-02 -2.31401143e-02 -2.75176461e-02
  -3.37378113e-02 -1.04769069e-01 -3.36737897e-02 -3.86974729e-02
  -8.71500817e-02  3.38203890e-02 -1.83569476e-02 -4.88653591e-01
   4.09923891e-02 -6.20894454e-02 -6.46112621e-02  3.82051988e-01
  -1.34332696e-01  1.49243156e-02 -2.59092055e-02  1.45457468e-01
  -2.00513017e-01 -2.99098945e-02 -4.37546854e-02  8.59870244e-02
   4.44191485e-02 -2.07461786e-01]
 [-1.48544269e-01  1.51489601e-02 -7.62814671e-02  3.08627769e-01
   1.75940802e-01  5.14423406e-02  4.71303119e-02  4.87007250e-02
  -2.67637656e-01  1.74869971e-01 -1.29479824e-01 -1.70692228e-02
   2.28283061e-01  2.90699601e-02 -8.39465659e-02  1.02403535e-01
   1.76594601e-01 -3.45256285e-01 -4.77535216e-02 -2.11182720e-02
   1.49663519e-01 -2.57271312e-01  8.13346820e-03  3.52417304e-02
   3.37438979e-04 -9.35866931e-02  8.22987937e-03  2.80511149e-01
   1.58291595e-01  1.48507228e-01 -3.96613670e-02  8.55423772e-02
  -2.63169307e-02 -1.65307485e-01 -4.88475397e-02  8.18288543e-02
   5.22455597e-02 -7.33381321e-02 -8.40647767e-02  3.65941124e-01
   1.90773862e-02  5.14079486e-02 -4.39843393e-02  2.19081531e-01
   1.51738794e-01 -2.75472643e-03]]
In [76]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s_2)
vars = pca.explained_variance_ratio_
c_names = chile_data_s_2.columns
sum = 0

print('Variance:  Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
    output = '{0:4.1f}%:    '.format(100.0 * vars[idx])
    output += " + ".joi`n("{0:5.2f} * {1:s}".format(val, name) \
                      for val, name in zip(row, c_names))
    sum += 100*vars[idx]
    print(output)

print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 83.6.0%
Variance:  Projected dimension
------------------------------
38.2%:    -0.13 * Density of restaurants and other food services per 100,000 inhabitants + -0.10 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.20 * Car rental companies + -0.01 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.06 * Density of beds in hospitals per 10,000 inhabitants +  0.01 * Number of spas + -0.14 * Density of gambling casinos per million inhabitants +  0.22 * Number of golf courses +  0.06 * Number of craft centers + -0.10 * Density of tour guides per 100,000 inhabitants +  0.00 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.10 * Penetration of telephone lines in service per 100 inhabitants +  0.23 * Density of service stations +  0.22 * Number of tour-operator companies certified with the tourism quality seal +  0.05 * Perception of exposure to crime (%) +  0.05 * Percentage of victimized households with at least one victim + -0.04 * Density of homicides per million inhabitants + -0.08 * Density of crimes against public health per million inhabitants +  0.05 * Black figure index +  0.22 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime +  0.23 * Number of declared crimes +  0.23 * Number of crimes investigated +  0.23 * Number of accidents (roads, air and waterways) +  0.21 * Illegal commerce +  0.23 * Number of Carabineros +  0.11 * Unemployment rate +  0.02 * Poverty rate + -0.03 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.23 * Number of strikes carried out + -0.03 * Average (days) duration of a strike +  0.21 * Person-day cost of a strike +  0.19 * Density of Bank Branches per million inhabitants +  0.22 * Floating population +  0.08 * Volume of exports + -0.13 * Density of Tourist Information Offices per million inhabitants +  0.17 * Number of visits to Tourist Information Offices +  0.21 * Average monthly global searches by tourist attraction on the internet + -0.06 * National tourism promotion budget (Thousands of USD) + -0.06 * International tourism promotion budget (Thousands of USD) +  0.22 * Investments in public infrastructure made by the Ministry of Public Works +  0.17 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.04 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.19 * Funds obtained from FNRD (Thousands of pesos) + -0.03 * Number of regional strategic development plans
16.6%:     0.23 * Density of restaurants and other food services per 100,000 inhabitants +  0.23 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.07 * Car rental companies +  0.17 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.26 * Density of beds in hospitals per 10,000 inhabitants + -0.20 * Number of spas +  0.26 * Density of gambling casinos per million inhabitants +  0.05 * Number of golf courses +  0.10 * Number of craft centers +  0.28 * Density of tour guides per 100,000 inhabitants + -0.19 * Number of thermal centers + -0.03 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.27 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations +  0.09 * Number of tour-operator companies certified with the tourism quality seal +  0.06 * Perception of exposure to crime (%) + -0.05 * Percentage of victimized households with at least one victim +  0.06 * Density of homicides per million inhabitants +  0.17 * Density of crimes against public health per million inhabitants +  0.04 * Black figure index + -0.02 * Budget for public safety (Thousands of $) + -0.10 * Percentage of households that reported at least one crime +  0.04 * Number of declared crimes +  0.04 * Number of crimes investigated +  0.04 * Number of accidents (roads, air and waterways) +  0.13 * Illegal commerce +  0.01 * Number of Carabineros + -0.12 * Unemployment rate + -0.33 * Poverty rate +  0.04 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.08 * Number of strikes carried out + -0.07 * Average (days) duration of a strike +  0.09 * Person-day cost of a strike +  0.04 * Density of Bank Branches per million inhabitants +  0.10 * Floating population +  0.06 * Volume of exports +  0.28 * Density of Tourist Information Offices per million inhabitants +  0.03 * Number of visits to Tourist Information Offices +  0.03 * Average monthly global searches by tourist attraction on the internet + -0.06 * National tourism promotion budget (Thousands of USD) +  0.30 * International tourism promotion budget (Thousands of USD) + -0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.06 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.15 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.09 * Funds obtained from FNRD (Thousands of pesos) + -0.19 * Number of regional strategic development plans
 8.8%:    -0.07 * Density of restaurants and other food services per 100,000 inhabitants + -0.09 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.08 * Car rental companies + -0.22 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.18 * Density of beds in hospitals per 10,000 inhabitants + -0.05 * Number of spas + -0.05 * Density of gambling casinos per million inhabitants + -0.00 * Number of golf courses + -0.24 * Number of craft centers + -0.07 * Density of tour guides per 100,000 inhabitants + -0.06 * Number of thermal centers +  0.28 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.07 * Penetration of telephone lines in service per 100 inhabitants + -0.07 * Density of service stations + -0.02 * Number of tour-operator companies certified with the tourism quality seal +  0.26 * Perception of exposure to crime (%) +  0.34 * Percentage of victimized households with at least one victim + -0.02 * Density of homicides per million inhabitants + -0.06 * Density of crimes against public health per million inhabitants +  0.35 * Black figure index + -0.08 * Budget for public safety (Thousands of $) +  0.01 * Percentage of households that reported at least one crime + -0.01 * Number of declared crimes + -0.06 * Number of crimes investigated + -0.03 * Number of accidents (roads, air and waterways) +  0.02 * Illegal commerce + -0.05 * Number of Carabineros + -0.05 * Unemployment rate +  0.01 * Poverty rate +  0.40 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.02 * Number of strikes carried out +  0.11 * Average (days) duration of a strike +  0.18 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.01 * Floating population +  0.12 * Volume of exports + -0.04 * Density of Tourist Information Offices per million inhabitants +  0.15 * Number of visits to Tourist Information Offices + -0.04 * Average monthly global searches by tourist attraction on the internet + -0.28 * National tourism promotion budget (Thousands of USD) + -0.10 * International tourism promotion budget (Thousands of USD) + -0.10 * Investments in public infrastructure made by the Ministry of Public Works + -0.15 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.20 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.13 * Funds obtained from FNRD (Thousands of pesos) +  0.05 * Number of regional strategic development plans
 6.2%:     0.03 * Density of restaurants and other food services per 100,000 inhabitants +  0.02 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.17 * Car rental companies +  0.23 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.21 * Density of beds in hospitals per 10,000 inhabitants +  0.22 * Number of spas + -0.04 * Density of gambling casinos per million inhabitants + -0.06 * Number of golf courses + -0.06 * Number of craft centers + -0.02 * Density of tour guides per 100,000 inhabitants +  0.03 * Number of thermal centers + -0.01 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.04 * Penetration of telephone lines in service per 100 inhabitants + -0.08 * Density of service stations + -0.05 * Number of tour-operator companies certified with the tourism quality seal +  0.31 * Perception of exposure to crime (%) + -0.11 * Percentage of victimized households with at least one victim +  0.08 * Density of homicides per million inhabitants + -0.22 * Density of crimes against public health per million inhabitants + -0.22 * Black figure index +  0.01 * Budget for public safety (Thousands of $) +  0.36 * Percentage of households that reported at least one crime + -0.05 * Number of declared crimes + -0.07 * Number of crimes investigated + -0.05 * Number of accidents (roads, air and waterways) + -0.09 * Illegal commerce + -0.07 * Number of Carabineros +  0.09 * Unemployment rate + -0.01 * Poverty rate + -0.24 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.02 * Number of strikes carried out +  0.24 * Average (days) duration of a strike + -0.05 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.04 * Floating population +  0.35 * Volume of exports + -0.05 * Density of Tourist Information Offices per million inhabitants +  0.31 * Number of visits to Tourist Information Offices + -0.03 * Average monthly global searches by tourist attraction on the internet + -0.01 * National tourism promotion budget (Thousands of USD) +  0.03 * International tourism promotion budget (Thousands of USD) + -0.11 * Investments in public infrastructure made by the Ministry of Public Works +  0.16 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.21 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.01 * Funds obtained from FNRD (Thousands of pesos) + -0.06 * Number of regional strategic development plans
 5.8%:     0.12 * Density of restaurants and other food services per 100,000 inhabitants + -0.05 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.06 * Car rental companies + -0.02 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.05 * Density of beds in hospitals per 10,000 inhabitants + -0.06 * Number of spas +  0.11 * Density of gambling casinos per million inhabitants + -0.02 * Number of golf courses + -0.01 * Number of craft centers +  0.06 * Density of tour guides per 100,000 inhabitants + -0.01 * Number of thermal centers +  0.28 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.06 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + -0.07 * Number of tour-operator companies certified with the tourism quality seal +  0.21 * Perception of exposure to crime (%) +  0.10 * Percentage of victimized households with at least one victim +  0.40 * Density of homicides per million inhabitants + -0.17 * Density of crimes against public health per million inhabitants + -0.11 * Black figure index +  0.06 * Budget for public safety (Thousands of $) + -0.23 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes +  0.01 * Number of crimes investigated + -0.06 * Number of accidents (roads, air and waterways) + -0.06 * Illegal commerce + -0.01 * Number of Carabineros + -0.31 * Unemployment rate +  0.03 * Poverty rate +  0.08 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.05 * Number of strikes carried out +  0.00 * Average (days) duration of a strike + -0.03 * Person-day cost of a strike +  0.17 * Density of Bank Branches per million inhabitants + -0.07 * Floating population +  0.09 * Volume of exports +  0.10 * Density of Tourist Information Offices per million inhabitants + -0.00 * Number of visits to Tourist Information Offices +  0.18 * Average monthly global searches by tourist attraction on the internet +  0.11 * National tourism promotion budget (Thousands of USD) +  0.11 * International tourism promotion budget (Thousands of USD) +  0.07 * Investments in public infrastructure made by the Ministry of Public Works +  0.26 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.29 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.19 * Funds obtained from FNRD (Thousands of pesos) +  0.39 * Number of regional strategic development plans
 5.0%:    -0.01 * Density of restaurants and other food services per 100,000 inhabitants +  0.10 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.13 * Car rental companies +  0.02 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.02 * Density of beds in hospitals per 10,000 inhabitants + -0.20 * Number of spas + -0.15 * Density of gambling casinos per million inhabitants +  0.05 * Number of golf courses +  0.36 * Number of craft centers + -0.21 * Density of tour guides per 100,000 inhabitants + -0.08 * Number of thermal centers + -0.19 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.03 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.11 * Number of tour-operator companies certified with the tourism quality seal +  0.15 * Perception of exposure to crime (%) +  0.26 * Percentage of victimized households with at least one victim +  0.10 * Density of homicides per million inhabitants + -0.08 * Density of crimes against public health per million inhabitants + -0.08 * Black figure index + -0.08 * Budget for public safety (Thousands of $) + -0.05 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes + -0.03 * Number of crimes investigated + -0.03 * Number of accidents (roads, air and waterways) + -0.10 * Illegal commerce + -0.03 * Number of Carabineros + -0.04 * Unemployment rate + -0.09 * Poverty rate +  0.03 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.02 * Number of strikes carried out + -0.49 * Average (days) duration of a strike +  0.04 * Person-day cost of a strike + -0.06 * Density of Bank Branches per million inhabitants + -0.06 * Floating population +  0.38 * Volume of exports + -0.13 * Density of Tourist Information Offices per million inhabitants +  0.01 * Number of visits to Tourist Information Offices + -0.03 * Average monthly global searches by tourist attraction on the internet +  0.15 * National tourism promotion budget (Thousands of USD) + -0.20 * International tourism promotion budget (Thousands of USD) + -0.03 * Investments in public infrastructure made by the Ministry of Public Works + -0.04 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.04 * Funds obtained from FNRD (Thousands of pesos) + -0.21 * Number of regional strategic development plans
 4.3%:    -0.15 * Density of restaurants and other food services per 100,000 inhabitants +  0.02 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.08 * Car rental companies +  0.31 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.18 * Density of beds in hospitals per 10,000 inhabitants +  0.05 * Number of spas +  0.05 * Density of gambling casinos per million inhabitants +  0.05 * Number of golf courses + -0.27 * Number of craft centers +  0.17 * Density of tour guides per 100,000 inhabitants + -0.13 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.23 * Penetration of telephone lines in service per 100 inhabitants +  0.03 * Density of service stations + -0.08 * Number of tour-operator companies certified with the tourism quality seal +  0.10 * Perception of exposure to crime (%) +  0.18 * Percentage of victimized households with at least one victim + -0.35 * Density of homicides per million inhabitants + -0.05 * Density of crimes against public health per million inhabitants + -0.02 * Black figure index +  0.15 * Budget for public safety (Thousands of $) + -0.26 * Percentage of households that reported at least one crime +  0.01 * Number of declared crimes +  0.04 * Number of crimes investigated +  0.00 * Number of accidents (roads, air and waterways) + -0.09 * Illegal commerce +  0.01 * Number of Carabineros +  0.28 * Unemployment rate +  0.16 * Poverty rate +  0.15 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.04 * Number of strikes carried out +  0.09 * Average (days) duration of a strike + -0.03 * Person-day cost of a strike + -0.17 * Density of Bank Branches per million inhabitants + -0.05 * Floating population +  0.08 * Volume of exports +  0.05 * Density of Tourist Information Offices per million inhabitants + -0.07 * Number of visits to Tourist Information Offices + -0.08 * Average monthly global searches by tourist attraction on the internet +  0.37 * National tourism promotion budget (Thousands of USD) +  0.02 * International tourism promotion budget (Thousands of USD) +  0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.04 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.22 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.15 * Funds obtained from FNRD (Thousands of pesos) + -0.00 * Number of regional strategic development plans
Total variance explained by the 7 components 84.8%
In [79]:
pd.DataFrame(np.column_stack((chile_data_s_2.columns, pca.components_[0]))).sort_values(by = 1, ascending = False)
#above information, for the Principal Component 1 (sorted)
Out[79]:
0 1
22 Number of declared crimes 0.234363
26 Number of Carabineros 0.233118
24 Number of accidents (roads, air and waterways) 0.232392
23 Number of crimes investigated 0.230352
13 Density of service stations 0.229039
30 Number of strikes carried out 0.227837
34 Floating population 0.224281
41 Investments in public infrastructure made by t... 0.220136
7 Number of golf courses 0.219424
20 Budget for public safety (Thousands of $) 0.218917
14 Number of tour-operator companies certified wi... 0.217724
38 Average monthly global searches by tourist att... 0.212374
25 Illegal commerce 0.208387
32 Person-day cost of a strike 0.20688
2 Car rental companies 0.195812
44 Funds obtained from FNRD (Thousands of pesos) 0.193036
33 Density of Bank Branches per million inhabitants 0.190525
37 Number of visits to Tourist Information Offices 0.168545
42 Investment Initiatives in projects or programs... 0.168277
27 Unemployment rate 0.108428
12 Penetration of telephone lines in service per ... 0.104726
35 Volume of exports 0.0820219
8 Number of craft centers 0.0581024
4 Density of beds in hospitals per 10,000 inhabi... 0.0576553
16 Percentage of victimized households with at le... 0.0503569
19 Black figure index 0.0470673
15 Perception of exposure to crime (%) 0.0468836
28 Poverty rate 0.0185849
5 Number of spas 0.00668292
10 Number of thermal centers 0.00189879
3 Densidad de camas en hospitales por cada 10.00... -0.0100633
11 Density of Sports Facilities and Venues per 10... -0.0170435
29 Density of crimes against property law and ind... -0.0262714
31 Average (days) duration of a strike -0.0271694
45 Number of regional strategic development plans -0.0288824
21 Percentage of households that reported at leas... -0.0348171
43 Contributions of government funds to the Touri... -0.0369478
17 Density of homicides per million inhabitants -0.0416695
39 National tourism promotion budget (Thousands o... -0.0644263
40 International tourism promotion budget (Thousa... -0.0644487
18 Density of crimes against public health per mi... -0.0776543
1 Density of People employed in restaurants and ... -0.0971941
9 Density of tour guides per 100,000 inhabitants -0.0980069
36 Density of Tourist Information Offices per mil... -0.126775
0 Density of restaurants and other food services... -0.133132
6 Density of gambling casinos per million inhabi... -0.139482
In [80]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s_2)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
 The Factor scores are
Out[80]:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
0 -2.373870 0.823256 0.472329 -2.079397 -2.919301 0.141580 0.186033
1 -1.485061 -0.247090 6.228244 -2.115655 1.203540 0.595868 0.757801
2 0.263871 2.084163 1.119329 4.384186 0.480078 3.195863 0.422146
3 -1.814625 0.019734 2.139679 2.229493 -0.772417 -1.927273 -0.772461
4 -0.969405 -1.998904 -0.011820 0.458845 1.718456 -1.177366 -1.155089
5 1.188020 0.340730 -0.711721 -0.291174 -0.750611 1.368108 0.495793
6 14.460293 2.855751 0.166888 -0.525576 -0.548933 -0.744969 -0.624666
7 -1.283626 -0.937935 -0.789281 -1.051599 -2.088445 1.379180 -1.114501
8 -0.680583 -3.431526 -0.366983 -0.213786 -1.694869 -0.554999 -0.381959
9 2.024578 -3.385638 -1.595265 -1.373500 2.088322 0.846775 3.060953
10 0.412162 -4.107222 -0.063790 1.987826 -0.289221 -2.512417 0.329350
11 -1.888205 -1.361590 -2.279907 0.261054 -1.076617 1.509792 -0.109209
12 0.031550 -0.800397 -1.746730 -0.594429 3.044853 -0.001577 -1.242050
13 -3.898966 3.945054 -0.990212 -1.273527 1.729195 0.068358 -2.559529
14 -3.986135 6.201613 -1.570759 0.197240 -0.124031 -2.186923 2.707388
In [82]:
# Example of different variables in each component
#visualize an example how variables can contribute to diffent principal components

# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s_2)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
    y_axis[i]=[np.mean(pca_model.components_[i][0:15]), np.mean(pca_model.components_[i][15:27]), 
               np.mean(pca_model.components_[i][27:36]), np.mean(pca_model.components_[i][36:41]),
               np.mean(pca_model.components_[i][41:46])]
# Plot
x_axis = ['TOURISM-RELATED SERVICES', 'SECURITY AND SAFETY ', 'ECONOMIC PERFORMANCE', 'TOURISM PROMOTION', 'GOVERNMENTAL INVOLVEMENT AND EFFICIENCY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass

3. Developing a scoring system for 5 dimensions

Step 1 - Calculate a weighted average for each variable in principal components.

Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.

In [83]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s_2.columns, pca_model.components_[0] * 
                                        pca_model.explained_variance_ratio_[0],
                                        pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
                                        pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
                                        pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
                                        pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
                                        pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
                                        pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)

# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)

# Print
weights.head()
Out[83]:
1 2 3 4 5 6 7 weighted_average
0
Density of restaurants and other food services per 100,000 inhabitants -0.0508779 0.0383434 -0.00603016 0.00173496 0.00703431 -0.000705732 -0.00638432 -0.019904
Density of People employed in restaurants and the like per 10,000 inhabitants -0.0371438 0.0379041 -0.00768655 0.00145828 -0.00263994 0.00488203 0.000651091 -0.003035
Car rental companies 0.0748317 0.0115487 -0.0072556 0.0104844 0.00355798 -0.00633721 -0.00327852 0.098485
Densidad de camas en hospitales por cada 10.000 habitantes -0.00384581 0.0285705 -0.0193489 0.0143164 -0.00104324 0.00105819 0.0132646 0.038865
Density of beds in hospitals per 10,000 inhabitants 0.0220336 0.0438976 0.0153522 0.0128237 0.00260291 0.000892908 0.0075618 0.123961

Step 2. Calculate a score for each dimension.

Multiply weighted average of a variable by each standartized value in each column and sum up results, receiving a final score.

In [84]:
# Example
chile_data_s_2.head(1)
Out[84]:
Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
Region
Arica y Parinacota 0.087073 -0.79801 -0.411459 -0.820845 -0.633616 -0.60701 0.342739 -0.560316 -0.554658 -0.135893 0.057354 -1.871499 0.695488 -0.749525 0.121472 -0.248797 -0.093065 -1.087756 2.859839 1.648551 -0.996415 -0.65277 -0.512578 -0.565722 -0.513953 -0.258745 -0.663 0.812831 -0.834653 0.204305 -0.433573 -0.533574 -0.597159 -0.448859 -0.392595 -0.771859 0.417721 -0.484381 -0.554876 0.288149 -0.067919 -0.731979 -1.298074 -0.291691 -1.15157 -0.953463

As a result, we multiply:

  • weighted average for Density of restaurants and other food services per 100,000 inhabitants (0.087073) in the dataframe weights by each value in the Density of restaurants and other food services per 100,000 inhabitants column in the dataframe chile_data_s_2.

  • Do the same for all weighted averages and columns in respective dataframe

  • Sum up the product of multiplications and receive the score for the first dimension

In [85]:
# Ranking for dimension 6

# Create a dataframe for relevant variables
dim6 = chile_data_s_2.iloc[:, 0:15].mul(weights['weighted_average'][0:15], axis = 1)

# Create a score ranking
dim6['Ranking 6'] = dim6.sum(axis = 1)

# Sort by score
dim6.sort_values(by = 'Ranking 6', ascending = False).head()
Out[85]:
Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Ranking 6
Region
Metropolitana 0.027651 0.003034 0.291889 0.001947 0.163672 0.023149 0.022439 0.344744 0.024562 -0.000959 0.012079 -0.009206 0.259132 0.278443 0.351165 1.793741
Antofagasta -0.003268 -0.001249 0.054452 0.040697 0.205288 0.003858 0.000265 -0.020279 0.014184 -0.000202 0.042275 -0.010101 0.119666 -0.048206 -0.029492 0.367888
Valparaíso -0.019993 -0.003931 -0.002533 0.021514 0.056133 -0.034723 0.011771 0.162232 0.028021 -0.000602 0.027177 -0.000799 0.064596 0.049509 -0.019204 0.339170
Magallanes y Antártica -0.027274 -0.006771 -0.040523 0.081745 0.231735 0.023149 -0.050179 -0.060837 -0.022140 0.005464 0.072472 -0.001523 0.192925 -0.060770 -0.019204 0.318268
Biobío 0.022239 0.003707 -0.002533 0.016326 -0.140644 0.010288 0.013611 0.040558 -0.003113 -0.001116 0.027177 0.003036 -0.066064 0.071844 -0.039780 -0.044464
In [86]:
# Ranking for dimension 7

# Create a dataframe for relevant variables
dim7 = chile_data_s_2.iloc[:, 15:27].mul(weights['weighted_average'][15:27], axis = 1)

# Create a score ranking
dim7['Ranking 7'] = dim7.sum(axis = 1)

# Sort the by score
dim7.sort_values(by = 'Ranking 7', ascending = False).head()
Out[86]:
Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Ranking 7
Region
Metropolitana 0.047892 0.029820 -0.005519 0.017258 0.031286 0.289907 0.026840 0.384044 0.340181 0.350464 0.365357 0.330985 2.208516
Biobío 0.031120 0.045423 -0.005922 0.023287 0.001409 0.137902 0.040228 0.049240 0.074065 0.034232 -0.040694 0.063199 0.453489
Tarapacá 0.116375 0.209779 0.000929 0.023287 0.058774 -0.071726 0.051703 -0.035178 -0.060643 -0.048898 -0.010863 -0.049287 0.184253
Valparaíso -0.037363 0.025659 0.000868 0.005492 0.008579 0.002698 0.006758 0.027251 0.073708 0.053211 -0.033773 0.044516 0.177603
Antofagasta 0.291078 0.049584 0.007057 0.023287 -0.021896 -0.011193 -0.041057 -0.017101 -0.042283 -0.026907 -0.042304 -0.041806 0.126460
In [87]:
# Ranking for dimension 8

# Create a dataframe for relevant variables
dim8 = chile_data_s_2.iloc[:, 27:36].mul(weights['weighted_average'][27:36], axis = 1)

# Create a score ranking
dim8['Ranking 8'] = dim8.sum(axis = 1)

# Sort the dataframe by score
dim8.sort_values(by = 'Ranking 8', ascending = False).head()
Out[87]:
Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Ranking 8
Region
Metropolitana 0.020622 0.034039 -0.006377 0.418184 0.007745 0.419765 0.250552 0.386336 0.087579 1.618444
Antofagasta -0.000859 0.054850 -0.014695 0.006476 0.014940 0.026760 -0.018619 -0.030050 0.397214 0.436016
Tarapacá -0.020909 -0.029784 0.128731 -0.025905 0.000817 0.125953 -0.028268 -0.039164 0.000041 0.111514
Valparaíso 0.017758 -0.004810 -0.003026 -0.007401 0.012009 -0.018861 -0.026963 0.047685 0.031123 0.047514
Los Lagos -0.039526 -0.007585 -0.011188 -0.044409 0.019203 -0.067512 0.178138 -0.036873 -0.029088 -0.038840
In [88]:
# Ranking for dimension 9

# Create a dataframe for relevant variables
dim9 = chile_data_s_2.iloc[:, 36:41].mul(weights['weighted_average'][36:41], axis = 1)

# Create a score ranking
dim9['Ranking 9'] = dim9.sum(axis = 1)

# Sort the dataframe by score
dim9.sort_values(by = 'Ranking 9', ascending = False).head()
Out[88]:
Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Ranking 9
Region
Metropolitana 0.007732 0.297038 0.315846 0.051934 -1.807070e-18 0.672550
Antofagasta -0.000633 0.212435 -0.003504 -0.013374 2.520136e-03 0.197444
Coquimbo 0.004745 0.068566 0.070512 -0.008762 -6.927919e-03 0.128133
Atacama -0.000370 0.083933 -0.070812 0.034985 7.243356e-03 0.054979
Araucanía 0.005887 0.025383 -0.033017 0.028272 -1.256697e-02 0.013958
In [89]:
# Ranking for dimension 10

# Create a dataframe for relevant variables
dim10 = chile_data_s_2.iloc[:, 41:46].mul(weights['weighted_average'][41:46], axis = 1)

# Create a score ranking
dim10['Ranking 10'] = dim10.sum(axis = 1)

# Sort the dataframe by score
dim10.sort_values(by = 'Ranking 10', ascending = False).head()
Out[89]:
Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans Ranking 10
Region
Metropolitana 0.231341 0.146845 0.062107 0.172525 0.034371 0.647190
Los Lagos 0.039541 0.126108 -0.023540 0.060047 -0.017186 0.184970
Biobío 0.094131 0.030705 -0.138642 0.161778 -0.068743 0.079230
Antofagasta -0.053708 0.037057 0.045566 0.013379 0.034371 0.076666
Valparaíso 0.040477 0.000473 -0.032905 0.016581 0.034371 0.058999
In [90]:
# Create an aggregated dataframe with all scores
scoring_data2 = pd.concat([dim6.iloc[:,-1:], dim7.iloc[:,-1:], dim8.iloc[:,-1:], dim9.iloc[:,-1:], 
                           dim10.iloc[:,-1:]], axis = 1)

scoring_data2
Out[90]:
Ranking 6 Ranking 7 Ranking 8 Ranking 9 Ranking 10
Region
Arica y Parinacota -0.219562 -0.431607 -0.218964 -0.127449 -0.193358
Tarapacá -0.139428 0.184253 0.111514 -0.033586 -0.196716
Antofagasta 0.367888 0.126460 0.436016 0.197444 0.076666
Atacama -0.059748 -0.323617 -0.149980 0.054979 -0.156984
Coquimbo -0.327888 -0.296003 -0.271570 0.128133 -0.040475
Valparaíso 0.339170 0.177603 0.047514 -0.061269 0.058999
Metropolitana 1.793741 2.208516 1.618444 0.672550 0.647190
O'Higgins -0.319237 -0.415101 -0.064561 -0.184920 -0.053559
Maule -0.494721 -0.279228 -0.305912 -0.114380 -0.006079
Biobío -0.044464 0.453489 -0.071235 -0.087292 0.079230
Araucanía -0.402073 0.026774 -0.323501 0.013958 0.052154
Los Ríos -0.381948 -0.423620 -0.316565 -0.131099 -0.070488
Los Lagos -0.127404 -0.209206 -0.038840 -0.032185 0.184970
Aysén -0.302594 -0.322888 -0.210162 -0.088698 -0.261150
Magallanes y Antártica 0.318268 -0.475825 -0.242200 -0.206185 -0.120398
In [91]:
scoring_data2.style.highlight_null().render().split('\n')[:10]


def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

scoring_data2.style.\
    applymap(color_negative_red).\
    apply(highlight_max)
Out[91]:
Ranking 6 Ranking 7 Ranking 8 Ranking 9 Ranking 10
Region
Arica y Parinacota -0.219562 -0.431607 -0.218964 -0.127449 -0.193358
Tarapacá -0.139428 0.184253 0.111514 -0.033586 -0.196716
Antofagasta 0.367888 0.126460 0.436016 0.197444 0.076666
Atacama -0.059748 -0.323617 -0.149980 0.054979 -0.156984
Coquimbo -0.327888 -0.296003 -0.271570 0.128133 -0.040475
Valparaíso 0.339170 0.177603 0.047514 -0.061269 0.058999
Metropolitana 1.793741 2.208516 1.618444 0.672550 0.647190
O'Higgins -0.319237 -0.415101 -0.064561 -0.184920 -0.053559
Maule -0.494721 -0.279228 -0.305912 -0.114380 -0.006079
Biobío -0.044464 0.453489 -0.071235 -0.087292 0.079230
Araucanía -0.402073 0.026774 -0.323501 0.013958 0.052154
Los Ríos -0.381948 -0.423620 -0.316565 -0.131099 -0.070488
Los Lagos -0.127404 -0.209206 -0.038840 -0.032185 0.184970
Aysén -0.302594 -0.322888 -0.210162 -0.088698 -0.261150
Magallanes y Antártica 0.318268 -0.475825 -0.242200 -0.206185 -0.120398

4. PCA for 10 dimensions

In [92]:
chile_data_2 = chile_data_2.drop(['Region'], axis = 1)
In [93]:
# First, we need to combine two dataframes with 5 dimensions each into one
chile_data = pd.concat([chile_data_1.reset_index(drop = True), chile_data_2], axis = 1)

# Print
chile_data
Out[93]:
Region CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
0 Arica y Parinacota 31.0 2 0 30 28 4 22.1 2 105 12 2 25.4 1 0 0 0 4 32 4 3 9 32 7 59 21.291667 21.9 0.46 58.00 1 0.00 5 1.138182 13 13 4 6 6 2 0 8 2 0 0 2 5.230769 0 1 42.556 0.88 293648 94.1 83.8 11.1 53.0 2 193 11 20.038 356 23.69 18.74 6544 33.01 37.0 2.3 5 3 0.0 0 97454 34186 1.151575e+06 5.2730 2.129 17.35 45248 15045 13 167211 4.67 3 35.857 19.563 12.0 21.619 43.766 0.0 5.273 1.0 20.0 20.038 5.0 0.000 240.55 17.0 6.0 0.318 0.260 42.184 15.819 0.513000 11884613.0 0.426 26467.0 2387.0 981.0 319.000000 265.0 7.3 8.2 84.369 1.0 11.0 253.0 94.915 45560.0 137.0 5.273 6517.0 1060490.0 193364.0 118413.000000 36370835.0 11757543.0 446922.0 24729573.0 1.0
1 Tarapacá 0.0 5 1 13 73 5 20.8 2 178 12 1 12.6 1 0 0 0 0 34 10 1 6 34 12 0 0.200000 9.1 0.03 76.03 1 0.00 16 1.138182 2 6 1 1 7 5 0 5 6 4 2 3 5.000000 0 0 68.563 1.45 381466 91.7 66.7 10.7 42.0 5 255 19 22.180 380 23.03 22.17 11108 41.43 42.8 2.1 5 10 0.0 11 235365 40919 1.956000e+04 4.1850 4.021 15.90 81182 17161 0 434727 184.10 1 25.947 26.616 2.0 18.958 60.264 0.0 4.185 3.0 9.0 22.180 5.0 40.134 213.07 28.0 0.0 0.421 0.468 87.884 0.000 0.516000 14740693.0 0.398 76896.0 1862.0 1105.0 593.000000 592.0 4.8 13.4 485.457 6.0 14.9 59745.0 138.104 83265.0 3236.0 4.185 7600.0 1750481.0 74404.0 13600.000000 42364100.0 12796789.0 355208.0 29714382.0 2.0
2 Antofagasta 1.0 9 0 28 81 16 27.4 8 203 15 2 5.7 2 1 0 3 1 24 37 0 4 31 26 63 21.291667 2.8 0.03 39.56 2 0.00 22 1.138182 5 28 8 13 3 14 1 6 10 6 4 0 5.000000 0 0 54.486 1.08 475866 91.7 66.7 10.6 40.0 7 529 47 20.446 184 24.55 22.76 19920 35.02 44.6 1.8 2 10 17.0 15 413922 84195 2.289800e+04 2.0244 3.332 52.25 112607 315888 0 115100 23.00 5 37.248 35.446 22.0 29.960 69.233 3.0 4.049 3.0 36.0 20.446 2.0 14.292 259.23 39.0 2.0 0.546 0.314 109.315 0.000 0.381000 22383612.0 0.495 123017.0 4228.0 2324.0 7.000000 765.0 6.2 7.3 12.146 13.0 9.6 30220.0 178.143 238010.0 16666.0 4.049 16280.0 2846340.0 200000.0 149000.000000 38262875.0 29066423.0 192572.0 50841622.0 1.0
3 Atacama 8.0 10 0 8 35 7 20.0 0 144 12 0 7.5 0 0 0 0 0 18 5 0 2 29 7 33 21.291667 2.0 0.02 40.76 0 3.93 17 1.138182 7 23 2 7 2 10 0 3 13 1 3 2 5.000000 1 0 51.844 0.94 379971 89.1 73.2 10.3 47.0 1 108 16 18.479 578 19.43 18.49 9674 29.25 36.0 1.9 1 3 0.0 15 168508 14222 2.416000e+03 0.0000 3.635 24.39 55812 24873 0 2552 11.33 2 44.823 26.933 19.0 25.478 56.225 1.0 3.932 1.0 9.0 18.479 3.0 32.437 184.01 35.0 1.0 0.390 0.197 66.841 0.000 0.488000 17068077.0 0.533 21705.0 3125.0 965.0 14.000000 504.0 6.4 10.2 15.727 3.0 29.3 17860.0 145.477 53046.0 3456.0 3.932 11614.0 556668.0 94100.0 187035.000000 35948639.0 16986593.0 233225.0 30224699.0 2.0
4 Coquimbo 23.0 7 0 2 52 7 18.3 4 80 22 2 1.7 0 1 0 7 1 69 2 1 0 29 27 97 0.800000 0.4 0.35 25.45 1 0.00 37 1.500000 4 16 7 0 1 21 0 18 0 9 1 0 5.000000 2 1 56.498 0.92 338014 92.7 71.2 9.7 43.0 7 870 85 6.963 1074 15.50 15.63 24346 12.00 35.2 2.8 4 9 383.9 750 205850 25803 2.786000e+03 0.0000 1.912 283.37 116263 57234 0 15265 228.58 1 37.632 25.165 9.0 17.805 47.745 3.0 3.316 3.0 13.0 6.963 3.0 14.091 150.06 72.0 3.0 0.288 0.218 155.833 0.000 0.396000 20225710.0 0.449 69974.0 4203.0 1831.0 100.000000 1082.0 6.0 12.3 11.605 3.0 29.0 1431.0 112.730 275447.0 2229.0 1.658 11056.0 5364222.0 189900.0 72917.000000 64917630.0 33222779.0 673800.0 44790979.0 3.0
5 Valparaíso 14.0 37 2 24 161 12 25.7 7 322 56 7 3.2 3 3 1 4 2 48 21 4 46 90 36 720 6.500000 2.7 2.11 38.46 13 0.00 59 6.640000 13 71 3 0 3 14 3 22 1 0 0 14 5.000000 3 2 53.534 0.99 311264 92.9 73.5 10.6 27.0 21 3949 316 12.209 3661 17.21 13.39 44504 21.18 29.7 2.0 14 27 920.0 257 430436 106915 3.173090e+05 0.6494 0.197 280.95 316618 146161 31 436195 93.86 1 52.408 47.050 16.0 27.756 55.850 9.0 1.948 12.0 44.0 12.209 3.0 18.710 230.65 179.0 3.0 0.311 0.291 87.671 1.948 0.432000 24137492.0 0.445 236177.0 19176.0 6765.0 166.000000 2761.0 7.5 11.6 50.654 10.0 10.7 16641.0 143.520 1557887.0 4287.0 1.299 5649.0 3058423.0 129481.0 63996.000000 101802434.0 24239428.0 811772.0 51647041.0 1.0
6 Metropolitana 4.0 56 0 1 404 35 22.5 70 6558 127 25 4.1 30 0 0 3 2 5 9 4 4 85 112 274 6.900000 0.9 7.25 47.03 16 0.00 0 0.040000 3 3 0 3 4 0 5 9 0 0 0 0 5.000000 0 0 55.901 6.06 421484 93.1 70.5 11.2 19.0 30 12881 1261 4.834 15221 38.13 4.33 43634 9.32 59.8 2.2 15 133 754.2 236 448887 765681 1.091111e+06 1.2810 0.731 541.32 1306140 83459 0 1147039 248.50 1 9.223 16.914 47.0 25.508 65.499 0.0 0.000 21.0 42.0 4.834 4.0 14.717 331.61 507.0 39.0 0.372 0.295 65.334 0.660 0.470000 60400444.0 0.424 1146510.0 53517.0 23242.0 7605.000000 9385.0 7.7 8.8 39.596 102.0 12.3 147198.0 1295.113 7307884.0 6196.0 0.330 19352.0 13709951.0 56982.0 128705.928571 230562951.0 43552270.0 62052.0 90875903.0 1.0
7 O'Higgins 32.0 13 1 9 67 6 21.9 13 251 36 0 1.9 1 7 2 3 16 29 3 1 0 28 15 229 11.300000 2.8 0.92 27.15 4 1.28 12 2.470000 5 22 1 3 2 2 7 5 2 1 1 7 5.000000 0 0 54.662 0.62 308068 95.5 65.0 9.5 23.0 7 352 15 9.992 2833 17.46 8.01 14526 6.34 15.4 2.2 2 7 10.0 0 73478 7066 2.405341e+05 0.0000 1.352 134.50 164204 11073 0 0 62.12 0 18.959 49.422 6.0 19.946 43.427 0.0 2.562 3.0 39.0 9.992 2.0 14.950 117.30 114.0 3.0 0.203 0.220 51.241 0.000 0.396000 18182163.0 0.477 111020.0 4894.0 2451.0 762.000000 1136.0 6.0 9.9 11.529 7.0 10.2 26831.0 95.805 265601.0 3056.0 1.281 4700.0 754000.0 179000.0 6000.000000 50200910.0 17850727.0 444491.0 43045390.0 1.0
8 Maule 29.0 17 0 1 54 7 11.1 9 657 29 1 1.6 1 7 0 0 10 27 4 3 0 37 28 19 12.700000 0.6 0.41 32.86 5 0.00 18 0.820000 0 20 23 12 7 5 6 13 3 0 0 9 5.000000 0 0 48.164 0.24 244231 93.4 73.1 9.0 24.0 4 364 37 8.700 2898 14.49 6.10 12278 8.67 28.4 2.0 3 14 0.0 0 167293 8935 3.942000e+03 0.0000 1.480 197.65 185728 64500 0 853 232.90 2 15.637 19.943 10.0 23.852 35.899 9.0 1.101 1.0 24.0 8.700 5.0 25.383 109.73 130.0 0.0 0.245 0.227 27.530 1.101 0.427000 25936948.0 0.530 124820.0 6018.0 4771.0 194.000000 1875.0 6.4 15.8 11.012 5.0 10.7 1371.0 48.346 165417.0 1311.0 1.101 7183.0 647510.0 168850.0 8000.000000 97730159.0 19292650.0 486500.0 48826193.0 2.0
9 Biobío 5.0 32 0 0 59 20 18.9 12 488 63 7 3.9 10 0 0 0 4 13 23 2 0 10 54 135 20.700000 2.8 0.96 31.68 2 1.07 25 0.730000 3 7 19 3 2 4 3 2 0 1 8 0 5.000000 0 0 48.651 1.23 290367 92.7 71.1 9.9 23.0 17 4023 320 1.612 2334 16.04 4.93 20802 7.89 36.0 1.9 5 12 25.0 9 393481 31409 1.546000e+03 0.0000 1.021 400.89 312085 49841 0 828 642.26 1 14.128 14.004 16.0 27.160 38.194 2.0 1.612 6.0 26.0 1.612 3.0 20.531 162.84 211.0 1.0 0.360 0.310 63.925 0.000 0.420000 41208340.0 0.410 292281.0 19222.0 5713.0 37.000000 3193.0 7.9 15.8 12.892 12.0 11.8 15518.0 188.607 305920.0 4475.0 1.074 7362.0 3548528.0 317000.0 44000.000000 137998425.0 28228357.0 1646127.0 88172439.0 3.0
10 Araucanía 18.0 13 0 2 96 5 13.2 8 179 58 4 30.1 0 5 3 0 13 16 6 10 0 12 31 20 29.400000 9.6 0.43 36.79 6 0.00 25 0.160000 22 79 51 10 17 3 7 10 0 1 3 23 5.000000 0 1 52.282 2.30 251081 94.5 72.0 9.1 29.0 8 709 67 9.085 709 11.21 9.90 26140 16.09 37.4 2.5 15 7 24.0 25 200377 38524 1.307130e+05 0.0000 1.615 236.97 124956 228921 0 112246 469.74 3 15.986 15.687 25.0 25.887 40.826 23.0 2.300 2.0 16.0 9.085 13.0 22.736 135.88 118.0 5.0 0.381 0.288 78.203 1.150 0.369000 29000528.0 0.469 140063.0 8685.0 3161.0 323.000000 1550.0 8.0 18.1 20.701 4.0 30.3 5856.0 232.887 395583.0 372.0 1.150 9488.0 1842375.0 108799.0 27507.000000 67212297.0 31050589.0 552127.5 54529735.0 2.0
11 Los Ríos 3.0 9 0 1 33 8 16.2 3 94 12 2 16.7 2 1 0 1 0 10 5 2 0 10 19 55 46.100000 6.9 0.31 35.41 0 0.00 10 0.140000 2 31 13 3 14 4 0 1 0 5 1 0 5.230769 0 1 47.218 0.41 268648 93.6 64.8 9.3 31.0 4 444 58 8.698 868 17.28 18.91 12846 11.81 37.9 1.7 9 1 54.1 0 113900 16070 1.114500e+04 0.0000 2.019 133.02 51811 376 7 0 42.67 1 34.512 26.207 9.0 30.472 42.649 2.0 2.806 1.0 40.0 8.698 12.0 0.000 191.32 44.0 1.0 0.274 0.270 101.011 0.000 0.298000 15834434.0 0.440 22076.0 3235.0 818.0 29.000000 773.0 6.4 14.3 11.223 4.0 11.3 6171.0 51.623 71200.0 71.0 2.806 7109.0 1476220.0 253292.0 35000.000000 49376288.0 22065484.0 486875.0 32409855.0 1.0
12 Los Lagos 1.0 24 1 0 64 11 19.8 8 272 31 1 20.8 2 0 0 0 0 38 3 3 0 57 30 94 56.300000 15.9 0.18 34.77 1 0.00 18 0.000000 12 41 17 10 9 10 3 6 0 31 0 0 7.000000 3 1 54.555 14.97 291431 92.9 66.7 9.1 31.0 10 856 72 20.928 2672 14.08 23.97 35548 23.82 31.6 1.8 27 13 54.1 48 275043 105048 2.594110e+05 10.9299 1.893 135.48 129882 486725 28 192977 660.16 3 36.415 27.360 24.0 22.644 47.298 2.0 4.186 2.0 27.0 20.928 8.0 25.728 162.58 86.0 6.0 0.323 0.201 104.641 0.000 0.374000 19901362.0 0.426 85949.0 8729.0 2023.0 192.000000 1409.0 3.5 11.8 23.719 2.0 8.0 2160.0 994.622 122157.0 2251.0 4.186 4708.0 7108761.0 264511.0 96915.000000 101170739.0 40816121.0 737875.0 62581190.0 2.0
13 Aysén 0.0 4 0 2 17 0 14.8 0 49 12 0 21.8 0 0 0 0 0 24 2 0 1 20 10 0 44.400000 39.4 0.02 43.89 0 0.00 0 0.010000 21 93 25 8 6 5 23 3 0 8 3 6 6.000000 0 2 61.016 1.05 418044 95.3 68.5 9.5 66.0 3 64 6 67.765 83 6.83 37.60 8020 23.06 21.7 1.7 0 1 0.0 19 28584 10763 9.087600e+04 0.0000 4.089 3.70 19348 32030 0 56201 5.00 4 76.509 40.987 15.0 27.543 43.720 0.0 10.930 0.0 57.0 67.765 4.0 24.264 170.81 20.0 0.0 0.351 0.256 174.879 10.930 0.417643 15972532.0 0.445 14119.0 3349.0 422.0 795.461538 469.0 4.4 5.2 32.790 1.0 7.0 1953.0 106.083 13667.0 379.0 10.930 5660.0 200654.0 65000.0 454500.000000 32558406.0 13692259.0 794614.0 29462726.0 2.0
14 Magallanes y Antártica 0.0 3 0 2 45 6 27.7 2 4 14 0 22.7 3 0 0 0 2 16 16 0 2 24 11 34 20.200000 57.3 0.04 40.58 3 0.00 0 0.010000 19 62 23 10 0 17 20 10 0 0 1 1 5.000000 1 1 51.872 0.06 572203 93.6 77.6 10.2 60.0 6 380 55 137.244 222 12.94 41.53 12436 67.76 37.1 2.0 5 3 0.0 12 84034 109092 2.621900e+05 0.1650 3.632 6.02 46336 283629 18 388598 0.00 5 59.008 59.340 12.0 34.676 71.606 0.0 13.260 1.0 15.0 137.244 0.0 18.366 297.25 21.0 3.0 0.283 0.180 53.041 6.630 0.367000 20075842.0 0.414 8659.0 2527.0 660.0 795.461538 216.0 5.4 6.1 86.192 1.0 22.0 616.0 5.115 322800.0 397.0 13.260 4217.0 558678.0 266000.0 525000.000000 30955753.0 18036736.0 357752.0 30288105.0 1.0
In [94]:
# Dimensions
chile_data.shape
Out[94]:
(15, 128)
In [95]:
# Standardize data for applying PCA

# Create a copy
chile_data_s = chile_data.copy()

# Standardize
scaler = StandardScaler()
chile_data_s.loc[:, chile_data_s.columns != 'Region'] = scaler.fit_transform(chile_data_s.loc[:, 
                                                        chile_data_s.columns != 'Region'])

# Set region as an index column
chile_data_s = chile_data_s.set_index('Region')
In [96]:
# Print
chile_data_s
# We can see that data was standardized
Out[96]:
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES % OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES % AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans
Region
Arica y Parinacota 1.672984 -0.959349 -0.559017 2.121142 -0.617597 -0.721316 0.440365 -0.475143 -0.335687 -0.724558 -0.261024 1.389477 -0.368577 -0.668153 -0.454859 -0.679900 0.067176 0.326242 -0.616670 0.294619 0.360486 -0.136165 -0.834058 -0.355859 0.000000 0.643982 -0.248227 1.426648 -0.576151 -0.410550 -0.836955 0.000000 0.597479 -0.772105 -0.685892 0.015686 0.100452 -0.947748 -0.755610 -0.011653 -0.118729 -0.580615 -0.855528 -0.386889 1.657385e-15 -0.620174 0.476731 -1.851488 -0.362785 -0.632189 0.646530 2.619732 1.554178 1.133129 -0.884585 -0.477620 -0.477498 -0.135893 -0.524853 0.790598 0.091521 -1.132711 0.620125 0.166320 0.816497 -0.347974 -0.437516 -0.521005 -0.482777 -0.920689 -0.322261 2.514051e+00 1.231771 -0.062345 -0.946158 -0.519001 -0.762949 0.621100 -0.126593 -0.862524 0.528271 0.087073 -0.798010 -0.411459 -0.820845 -0.633616 -0.607010 0.342739 -0.560316 -0.554658 -0.135893 0.057354 -1.871499 0.695488 -0.749525 0.121472 -0.248797 -0.093065 -1.087756 2.859839 1.648551 -0.996415 -0.652770 -0.512578 -0.565722 -0.513953 -2.587452e-01 -0.663000 0.812831 -0.834653 0.204305 -0.433573 -0.533574 -0.597159 -0.448859 -0.392595 -0.771859 0.417721 -0.484381 -0.554876 0.288149 -6.791934e-02 -0.731979 -1.298074 -0.291691 -1.151570 -0.953463
Tarapacá -0.955183 -0.754748 1.118034 0.467040 -0.126575 -0.599746 0.164252 -0.475143 -0.289792 -0.724558 -0.424164 0.064193 -0.368577 -0.668153 -0.454859 -0.679900 -0.738938 0.453349 0.000000 -0.508888 0.094554 -0.051062 -0.638575 -0.688420 -1.306936 -0.162045 -0.490079 2.901672 -0.576151 -0.410550 -0.106280 0.000000 -0.942896 -1.025452 -0.911185 -1.160793 0.315706 -0.451833 -0.755610 -0.536045 0.898951 -0.060661 0.095059 -0.230042 -4.306269e-01 -0.620174 -0.953463 2.567121 -0.207694 0.357940 -0.936809 -0.869163 0.995791 0.344242 -0.494327 -0.458377 -0.451687 -0.072248 -0.518277 0.696840 0.413159 -0.752896 1.147544 0.759346 0.136083 -0.347974 -0.216654 -0.521005 -0.425756 0.095203 -0.285528 -6.097864e-01 0.863508 1.513222 -0.955497 -0.401928 -0.747731 -0.614762 0.777892 -0.044781 -0.792406 -0.462238 -0.260925 -1.375815 -1.416782 0.849682 -0.607010 0.025557 -0.186772 -1.336868 -0.072248 0.057354 2.063212 0.255363 -0.658989 -0.521615 1.049823 2.963394 0.083237 -0.549390 1.700415 -0.757515 -1.353955 -0.328183 -0.606462 -0.491469 -1.099479e-01 -0.515181 -1.141089 0.586808 3.657754 -0.229057 -0.038898 1.017526 -0.328069 -0.371529 0.000349 0.115821 -0.228403 -0.353907 -1.225117 -7.595427e-01 -0.616910 -1.189453 -0.545976 -0.899207 0.476731
Antofagasta -0.870404 -0.481948 -0.559017 1.926542 -0.039282 0.737525 1.566057 -0.112746 -0.274074 -0.626053 -0.261024 -0.650217 -0.233732 -0.267261 -0.454859 0.777029 -0.537409 -0.182187 2.775014 -0.910642 -0.082735 -0.178716 -0.091225 -0.333313 0.000000 -0.558761 -0.490079 -0.081919 -0.360095 -0.410550 0.292270 0.000000 -0.522794 -0.229219 -0.385501 1.662758 -0.545311 1.035911 -0.610300 -0.361247 1.916631 0.199315 1.045645 -0.700582 -4.306269e-01 -0.620174 -0.953463 0.175428 -0.308367 1.422279 -0.936809 -0.869163 0.856194 0.200808 -0.234155 -0.373331 -0.361350 -0.123770 -0.571981 0.912767 0.468484 -0.019562 0.746029 0.943388 -0.884538 -0.771186 -0.216654 -0.461755 -0.405021 1.410506 -0.049429 -6.005751e-01 0.132194 0.939456 -0.721374 -0.299545 1.400719 -0.614762 -0.302782 -0.778986 1.848947 0.164176 0.411478 0.552897 1.047141 1.656064 -0.101168 -0.014090 -0.186772 0.583102 -0.123770 -0.802955 -0.470321 0.994671 -0.568454 -0.307253 2.625818 0.700439 0.632374 -0.549390 -0.633488 -0.118212 1.075151 -0.159541 -0.422858 -0.270437 -4.281786e-01 -0.436977 -0.046894 -1.080675 -0.417550 0.057264 -0.711150 0.216182 -0.216088 -0.285070 3.346832 0.078083 1.823205 -0.034723 0.372564 1.339133e-01 -0.695652 0.511042 -0.996899 0.170390 -0.953463
Atacama -0.276947 -0.413748 -0.559017 -0.019460 -0.541216 -0.356606 -0.005664 -0.595941 -0.311168 -0.724558 -0.587304 -0.463849 -0.503423 -0.668153 -0.454859 -0.679900 -0.738938 -0.563509 -0.513892 -0.910642 -0.260023 -0.263819 -0.834058 -0.502412 0.000000 -0.609138 -0.495703 0.016253 -0.792208 3.443259 -0.039855 0.000000 -0.242726 -0.410181 -0.836088 0.250982 -0.760565 0.374691 -0.755610 -0.885639 2.679891 -0.450626 0.570352 -0.386889 -4.306269e-01 0.310087 -0.953463 -0.273450 -0.346460 0.341084 -2.652094 0.457025 0.437403 0.702827 -1.014671 -0.504003 -0.461366 -0.182215 -0.464025 0.185433 0.068078 -0.872233 0.384602 0.064074 -0.544331 -0.912257 -0.437516 -0.521005 -0.405021 -0.397285 -0.431178 -6.570959e-01 -0.553020 1.191780 -0.900815 -0.484583 -0.692266 -0.614762 -0.683313 -0.832171 -0.132068 0.584058 -0.236785 0.263591 0.043387 0.486545 -0.438396 -0.048199 -0.560316 -1.336868 -0.182215 -0.516185 1.308603 -0.210069 -0.601376 -0.414434 0.658976 -1.018820 -0.455958 -0.549390 1.216346 -0.562837 2.026759 -0.529991 -0.508452 -0.516855 -4.243772e-01 -0.554961 0.109420 -0.287937 -0.386717 -0.351766 1.787599 -0.119284 -0.307448 -0.388412 0.055168 0.045618 0.720348 -0.701621 -0.974568 3.848926e-01 -0.740085 -0.751536 -0.884185 -0.873371 0.476731
Coquimbo 0.994747 -0.618348 -0.559017 -0.603261 -0.355718 -0.356606 -0.366735 -0.354344 -0.351405 -0.396208 -0.261024 -1.064369 -0.503423 -0.267261 -0.454859 2.719600 -0.537409 2.677726 -0.822226 -0.508888 -0.437311 -0.263819 -0.052129 -0.141667 -1.269757 -0.709891 -0.310096 -1.236250 -0.576151 -0.410550 1.288645 0.223127 -0.662828 -0.663528 -0.460599 -1.396089 -0.975820 2.193046 -0.755610 1.736318 -0.627570 0.589280 -0.380235 -0.700582 -4.306269e-01 1.240347 0.476731 0.517268 -0.351901 -0.131972 -0.277084 0.048967 -0.400178 0.415959 -0.234155 -0.267490 -0.238749 -0.524389 -0.328122 -0.372854 -0.200109 0.348769 -0.695918 -0.017723 2.517531 -0.489045 -0.248206 0.816999 3.405012 -0.122213 -0.367996 -6.560749e-01 -0.553020 -0.243053 0.767224 -0.287633 -0.459525 -0.614762 -0.640329 0.157934 -0.792406 0.185461 -0.371418 -0.700765 -1.674999 -0.275873 -0.101168 -0.227780 -0.186772 -1.052428 -0.524389 -0.516185 -0.490027 -0.753819 -0.296847 -0.200071 -0.627036 -0.710235 1.824327 -0.549390 -0.374165 -0.298713 -0.076796 -0.353494 -0.424798 -0.359829 -3.776744e-01 -0.293679 -0.203208 0.286115 -0.422208 -0.351766 1.749547 -0.565187 -0.399034 -0.264153 -0.250575 -0.585376 0.588459 0.698645 0.244084 -3.681311e-01 -0.183891 0.945462 0.337347 -0.135932 1.906925
Valparaíso 0.231731 1.427656 2.795085 1.537341 0.833647 0.251245 1.204986 -0.173145 -0.199258 0.720180 0.554676 -0.909062 -0.098887 0.534522 0.682288 1.262672 -0.335881 1.343100 1.130561 0.696373 3.640321 2.331823 0.299739 3.369953 -0.916559 -0.565058 0.679811 -0.171909 2.016529 -0.410550 2.749996 3.392878 0.597479 1.327055 -0.760990 -1.396089 -0.545311 1.035911 -0.319681 2.435507 -0.373149 -0.580615 -0.855528 1.495273 -4.306269e-01 2.170608 1.906925 0.013683 -0.332855 -0.433572 -0.145139 0.518234 0.856194 -0.731513 1.587050 0.688186 0.506535 -0.368515 0.380711 -0.129935 -0.410158 2.026315 -0.120893 -0.580075 -0.204124 0.921662 0.319723 2.685464 0.849439 1.532153 0.074523 2.118631e-01 -0.333213 -1.671223 0.751637 0.365125 0.180040 2.332293 0.782855 -0.456045 -0.792406 1.004495 1.295121 -0.025716 0.553550 0.452829 0.910515 -0.626589 1.494175 1.151982 -0.368515 -0.516185 -0.037183 0.536928 0.583817 -0.200071 -0.337053 0.362465 0.077779 -0.129568 0.248209 0.028493 -0.176966 0.254231 0.737122 0.534818 -3.418327e-01 0.465305 0.969144 0.094764 -0.085988 -0.065445 -0.571626 -0.152369 -0.312921 0.452371 0.262237 -0.684992 -0.689542 0.027050 -0.524494 -4.269976e-01 0.524283 0.006527 0.719887 0.211166 -0.953463
Metropolitana -0.616065 2.723459 -0.559017 -0.700561 3.485167 3.047358 0.525323 3.632022 3.721352 3.051461 3.491193 -0.815878 3.541940 -0.668153 -0.454859 0.777029 -0.335881 -1.389706 -0.102778 0.696373 -0.082735 2.119066 3.271070 0.856016 -0.891773 -0.678406 3.570787 0.529198 2.664699 -0.410550 -1.169080 -0.677230 -0.802862 -1.134029 -0.986283 -0.690201 -0.330057 -1.278358 -0.029062 0.163144 -0.627570 -0.580615 -0.855528 -0.700582 -4.306269e-01 -0.620174 -0.953463 0.415838 1.046635 0.809134 -0.013194 -0.093853 1.693775 -1.305249 2.757824 3.460544 3.555423 -0.587648 3.548131 2.841909 -1.259731 1.953914 -0.863790 2.497524 0.476290 1.062732 3.664195 2.107602 0.740581 1.668068 3.668526 2.347199e+00 -0.119431 -1.226533 2.428629 3.588997 -0.270914 -0.614762 3.186253 0.248718 -0.792406 -1.389249 -0.999731 2.963788 0.050106 1.320349 -0.607010 -1.194483 3.175121 1.009762 -0.587648 -0.229416 -0.428654 2.153925 3.283423 3.658450 0.432033 0.421243 -0.494572 -0.407151 0.905159 3.061756 -0.702855 3.582885 3.402018 3.522474 3.697959e+00 3.459654 1.125458 -0.670638 -0.181200 3.697638 -0.368682 3.391107 2.907840 3.665005 0.737921 -0.953872 2.549305 3.129454 -1.446739 -9.602287e-17 2.996435 2.025096 -1.358777 2.197183 -0.953463
O'Higgins 1.757764 -0.209147 1.118034 0.077840 -0.192044 -0.478176 0.397886 0.189252 -0.243896 0.063481 -0.587304 -1.043661 -0.368577 2.138090 1.819435 0.777029 2.485518 0.135581 -0.719448 -0.508888 -0.437311 -0.306371 -0.521286 0.602368 -0.619129 -0.558761 0.010499 -1.097174 0.072019 0.844635 -0.371980 0.821310 -0.522794 -0.446373 -0.911185 -0.690201 -0.760565 -0.947748 0.261557 -0.536045 -0.118729 -0.450626 -0.380235 0.397345 -4.306269e-01 -0.620174 -0.953463 0.205331 -0.433528 -0.469607 1.570145 -1.216013 -0.679371 -1.018381 -0.234155 -0.428269 -0.464593 -0.434389 0.153841 -0.094421 -0.914651 -0.468450 -1.050453 -2.042190 0.476290 -0.771186 -0.311309 -0.486152 -0.482777 -1.097303 -0.470219 8.031311e-17 -0.553020 -0.709394 -0.191618 -0.131441 -0.791516 -0.614762 -0.691941 -0.600698 -1.452744 -0.849583 1.475749 -0.990072 -1.195517 -0.664094 -0.607010 -0.447591 -0.186772 0.796432 -0.434389 -0.802955 -0.405811 -1.278511 0.048834 -0.200071 -1.698713 -0.680846 -0.855684 -0.549390 -0.374165 -0.469648 0.624389 -0.203408 -0.371176 -0.247409 -1.817148e-02 -0.269268 -0.203208 -0.369944 -0.422863 -0.188154 -0.635046 0.124200 -0.446370 -0.269654 -0.044503 -0.689987 -0.913848 -0.644146 0.105427 -8.096924e-01 -0.466447 -0.661217 -0.298431 -0.224305 -0.953463
Maule 1.503425 0.063653 -0.559017 -0.700561 -0.333895 -0.356606 -1.895977 -0.052346 0.011359 -0.166364 -0.424164 -1.074722 -0.368577 2.138090 -0.454859 -0.679900 1.276347 0.008474 -0.616670 0.294619 -0.437311 0.076593 -0.013032 -0.581324 -0.532379 -0.697297 -0.276349 -0.630042 0.288076 -0.410550 0.026570 -0.196217 -1.222965 -0.518758 0.740964 1.427462 0.315706 -0.451833 0.116248 0.862333 0.135691 -0.580615 -0.855528 0.711039 -4.306269e-01 -0.620174 -0.953463 -0.898684 -0.536922 -1.189355 0.184723 0.436622 -1.377355 -0.946664 -0.624413 -0.424545 -0.393613 -0.472778 0.171651 -0.516332 -1.093755 -0.655528 -0.904505 -0.712994 -0.204124 -0.630116 -0.090448 -0.521005 -0.482777 -0.406235 -0.460022 -6.528849e-01 -0.553020 -0.602802 0.215118 -0.061316 -0.407268 -0.614762 -0.689057 0.177622 -0.132068 -1.033721 -0.769073 -0.604330 -0.320759 -1.340920 0.910515 -0.873512 -0.560316 -0.270218 -0.472778 0.057354 0.617034 -1.399753 0.180522 -0.521615 -1.169178 -0.577984 -1.463242 -0.312109 0.161768 0.179011 1.951632 -0.152948 -0.283952 0.173260 -3.266272e-01 0.064793 0.109420 1.242867 -0.427314 -0.269960 -0.571626 -0.566815 -0.579103 -0.325629 -0.479322 -0.739933 -0.326965 -0.675162 -0.023689 -7.964951e-01 0.446097 -0.510508 -0.181958 0.068356 0.476731
Biobío -0.531285 1.086656 -0.559017 -0.797861 -0.279337 1.223806 -0.239298 0.128852 -0.094893 0.950024 0.554676 -0.836585 0.845031 -0.668153 -0.454859 -0.679900 0.067176 -0.881277 1.336118 -0.107134 -0.437311 -1.072298 1.003476 0.072525 -0.036662 -0.558761 0.032997 -0.726577 -0.360095 0.638706 0.491545 -0.251719 -0.802862 -0.989259 0.440573 -0.690201 -0.760565 -0.617138 -0.319681 -1.060436 -0.627570 -0.450626 2.946818 -0.700582 -4.306269e-01 -0.620174 -0.953463 -0.815943 -0.267554 -0.669181 -0.277084 0.028564 -0.120984 -1.018381 1.066706 0.711154 0.519440 -0.683383 0.017116 -0.296143 -1.203468 0.053838 -0.953363 0.064074 -0.544331 -0.347974 -0.153551 -0.433872 -0.436124 1.259932 -0.337412 -6.594967e-01 -0.553020 -0.985035 1.524147 0.350357 -0.512695 -0.614762 -0.689141 2.043257 -0.792406 -1.117365 -1.221327 -0.025716 0.420075 -1.134581 -0.269782 -0.724542 0.373544 -0.127998 -0.683383 -0.516185 0.141347 -0.549132 0.847193 -0.414434 0.280737 0.641661 -0.530676 -0.549390 0.040751 1.456407 -1.053447 0.459377 0.740691 0.344066 -4.118869e-01 0.660589 1.281772 1.242867 -0.411127 0.016361 -0.432102 -0.182849 -0.186823 -0.247127 0.309082 -0.747425 -0.284657 0.169799 1.860898 -5.589440e-01 1.219232 0.423448 3.033208 2.060316 1.906925
Araucanía 0.570849 -0.209147 -0.559017 -0.603261 0.124392 -0.599746 -1.449948 -0.112746 -0.289163 0.785850 0.065256 1.876105 -0.503423 1.336306 2.956582 -0.679900 1.880932 -0.690616 -0.411113 3.106895 -0.437311 -0.987195 0.104257 -0.575688 0.502429 -0.130560 -0.265100 -0.308531 0.504132 -0.410550 0.491545 -0.603228 1.857786 1.616595 2.843699 0.956870 2.468250 -0.782443 0.261557 0.337941 -0.627570 -0.450626 0.570352 2.906894 -4.306269e-01 -0.620174 0.476731 -0.199033 0.023581 -1.112122 0.910420 0.212190 -1.237758 -0.588079 -0.104069 -0.317462 -0.296823 -0.461338 -0.428131 -0.982281 -0.737422 0.498065 -0.439725 0.207218 1.496910 1.062732 -0.311309 -0.437358 -0.353184 -0.162528 -0.298595 -3.030555e-01 -0.553020 -0.490380 0.468371 -0.259311 0.775251 -0.614762 -0.312432 1.257007 0.528271 -1.014376 -1.093167 0.842204 0.134984 -0.897944 3.271109 -0.523971 -0.373544 -0.839098 -0.461338 2.351511 0.357523 -0.980929 0.081756 0.014291 0.545505 0.318381 -0.164824 -0.301548 -0.840946 0.435268 0.424050 -0.097212 -0.076991 -0.118670 -2.565730e-01 -0.082122 1.359928 1.871590 -0.343890 -0.310863 1.914439 -0.445087 -0.062981 -0.197030 -0.713302 -0.726337 0.217846 -0.327141 -0.787585 -6.677754e-01 -0.139835 0.718426 0.000000 0.357106 0.476731
Los Ríos -0.700845 -0.481948 -0.559017 -0.700561 -0.563039 -0.235036 -0.812764 -0.414743 -0.342603 -0.724558 -0.261024 0.488698 -0.233732 -0.267261 -0.454859 -0.194257 -0.738938 -1.071938 -0.513892 -0.107134 -0.437311 -1.072298 -0.364900 -0.378406 1.537238 -0.300581 -0.332594 -0.421428 -0.792208 -0.410550 -0.504830 -0.615562 -0.942896 -0.120641 -0.010013 -0.690201 1.822487 -0.617138 -0.755610 -1.235233 -0.627570 0.069327 -0.380235 -0.700582 1.657385e-15 -0.620174 0.476731 -1.059410 -0.490667 -0.914058 0.316668 -1.256818 -0.958565 -0.444645 -0.624413 -0.399714 -0.325860 -0.472837 -0.384566 -0.119991 0.107463 -0.608259 -0.707819 0.258341 -1.224745 0.216308 -0.500619 -0.332450 -0.482777 -0.799543 -0.421096 -6.330079e-01 -0.553020 -0.153948 -0.201151 -0.497619 -0.868449 0.050702 -0.691941 -0.689341 -0.792406 0.012520 -0.292070 -0.700765 1.161805 -0.734043 -0.269782 -0.376458 -0.560316 0.867542 -0.472837 2.064742 -1.871499 -0.092990 -0.527301 -0.414434 -0.803547 0.053880 0.419596 -0.549390 -2.068406 -0.666027 -0.302177 -0.528634 -0.499916 -0.543509 -4.162314e-01 -0.433361 0.109420 0.832830 -0.425497 -0.310863 -0.495522 -0.436538 -0.569938 -0.378269 -0.788305 -0.266827 -0.344456 -0.433789 1.050481 -6.183318e-01 -0.482279 -0.220693 -0.180918 -0.762745 -0.953463
Los Lagos -0.870404 0.541054 1.118034 -0.797861 -0.224779 0.129675 -0.048143 -0.112746 -0.230693 -0.100694 -0.424164 0.913203 -0.233732 -0.668153 -0.454859 -0.679900 -0.738938 0.707564 -0.719448 0.294619 -0.437311 0.927623 0.065161 -0.158577 2.169276 0.266157 -0.405712 -0.473786 -0.576151 -0.410550 0.026570 -0.701897 0.457445 0.241283 0.290378 0.956870 0.746215 0.374691 -0.319681 -0.361247 -0.627570 3.449024 -0.855528 -0.700582 3.301473e+00 2.170608 0.476731 0.187151 3.470945 -0.657185 -0.145139 -0.869163 -1.237758 -0.444645 0.156103 -0.271835 -0.280691 -0.109448 0.109727 -0.574576 0.581948 1.280998 0.044474 -0.385808 -0.884538 2.755581 -0.122000 -0.332450 -0.233959 0.387483 0.064338 5.209139e-02 3.146505 -0.258875 -0.185306 -0.243263 2.629382 2.047094 -0.039476 2.124835 0.528271 0.118003 -0.204269 0.745769 -0.591294 -0.316062 -0.269782 0.025849 -0.373544 -0.056888 -0.109448 0.917663 0.650857 -0.553296 -0.181620 0.121472 -0.185757 -0.960042 0.512610 -0.549390 -0.754505 -0.325844 -0.652770 -0.295081 -0.073576 -0.325015 -3.277133e-01 -0.145860 -2.157128 0.149436 -0.317904 -0.392670 -0.914094 -0.545401 2.067430 -0.349799 -0.245093 0.116098 -0.911957 1.206767 1.193196 -2.097769e-01 0.512154 1.739115 0.515001 0.764723 0.476731
Aysén -0.955183 -0.822948 -0.559017 -0.603261 -0.737624 -1.207597 -1.110116 -0.595941 -0.370895 -0.724558 -0.587304 1.016741 -0.503423 -0.668153 -0.454859 -0.679900 -0.738938 -0.182187 -0.822226 -0.910642 -0.348667 -0.646783 -0.716768 -0.688420 1.431898 1.745972 -0.495703 0.272316 -0.792208 -0.410550 -1.169080 -0.695731 1.717752 2.123289 0.891159 0.486278 0.100452 -0.451833 2.586510 -0.885639 -0.627570 0.459292 0.570352 0.240498 1.435423e+00 -0.620174 1.906925 1.284880 -0.316530 0.770348 1.438200 -0.501911 -0.679371 2.065449 -0.754499 -0.517660 -0.493630 1.282215 -0.599654 -1.604493 1.860058 -1.009879 -0.003132 -1.398041 -1.224745 -1.053328 -0.500619 -0.521005 -0.384286 -1.428005 -0.450049 -4.129872e-01 -0.553020 1.569849 -1.034075 -0.603383 -0.640792 -0.614762 -0.501923 -0.861020 1.188609 2.340413 0.833425 -0.122152 0.505849 -0.637751 -0.607010 1.991907 -0.747087 2.076412 1.282215 -0.229416 0.507328 -0.421483 -0.724833 -0.521615 0.167266 -0.151843 2.312351 1.806187 0.000000 -0.654476 -0.176966 -0.557729 -0.491070 -0.615313 -6.173829e-17 -0.570783 -1.453717 -1.654726 -0.239801 -0.433573 -1.040934 -0.551019 -0.417625 -0.410414 -0.711558 1.987437 -0.686942 -0.805315 -1.344744 2.149798e+00 -0.805176 -1.095859 0.672315 -0.911947 0.476731
Magallanes y Antártica -0.955183 -0.891148 -0.559017 -0.603261 -0.432100 -0.478176 1.629775 -0.475143 -0.399186 -0.658888 -0.587304 1.109925 -0.098887 -0.668153 -0.454859 -0.679900 -0.335881 -0.690616 0.616670 -0.910642 -0.260023 -0.476577 -0.677672 -0.496775 -0.067645 2.873150 -0.484454 0.001527 -0.144038 -0.410550 -1.169080 -0.695731 1.437684 1.001324 0.740964 0.956870 -1.191074 1.531826 2.150581 0.337941 -0.627570 -0.580615 -0.380235 -0.543736 -4.306269e-01 0.310087 0.476731 -0.268692 -0.585898 2.508457 0.316668 1.354752 0.297807 1.635147 -0.364241 -0.419578 -0.335539 3.346638 -0.561569 -0.736521 2.228582 -0.642380 2.796824 0.176544 -0.204124 -0.347974 -0.437516 -0.521005 -0.420572 -1.019545 0.086400 5.976015e-02 -0.497172 1.189281 -1.019132 -0.515456 1.168712 1.096431 0.621928 -0.883807 1.848947 1.370333 2.231003 -0.411459 2.103300 1.869415 -0.607010 2.671164 -0.560316 -0.910208 3.346638 -1.376494 -0.070909 1.603608 -0.716603 -0.200071 -0.690076 -1.268627 -0.809562 0.879473 -0.875522 -0.311249 -0.953278 -0.577694 -0.554858 -0.572158 -6.173829e-17 -0.685150 -0.672149 -1.408704 0.220001 -0.433573 0.861667 -0.587307 -0.700011 -0.237696 -0.707072 2.633969 -1.028010 -0.701036 1.212137 2.615002e+00 -0.835946 -0.641776 -0.538923 -0.870161 -0.953463
In [97]:
# Calculate eigenvalues and vectors
cov_mat = np.cov(chile_data_s.T)
eig_val, eig_vec = np.linalg.eig(cov_mat)

# Print 
print('Eigenvectors \n%s' %eig_vec)
print('\nEigenvalues \n%s' %eig_val)
Eigenvectors 
[[-0.01022173+0.j  0.14285067+0.j -0.05784359+0.j ... -0.01858377+0.j
  -0.00991488+0.j -0.02262521+0.j]
 [ 0.14103736+0.j  0.02401269+0.j  0.04968103+0.j ... -0.00698431+0.j
   0.00050155+0.j -0.01439759+0.j]
 [ 0.0142111 +0.j  0.03064968+0.j -0.00099107+0.j ... -0.00256709+0.j
  -0.03854842+0.j  0.00493263+0.j]
 ...
 [-0.01871132+0.j  0.09444283+0.j  0.07914181+0.j ...  0.09367178+0.j
   0.12321427+0.j -0.02054219+0.j]
 [ 0.12292742+0.j  0.05445328+0.j  0.0668449 +0.j ... -0.03881707+0.j
   0.05321002+0.j  0.00253113+0.j]
 [-0.02281744+0.j  0.10240462+0.j  0.00371734+0.j ... -0.07188785+0.j
   0.16661665+0.j -0.01150759+0.j]]

Eigenvalues 
[ 4.50766815e+01+0.00000000e+00j  1.83868353e+01+0.00000000e+00j
  1.23853547e+01+0.00000000e+00j  1.16143194e+01+0.00000000e+00j
  9.29727743e+00+0.00000000e+00j  6.89218885e+00+0.00000000e+00j
  6.39938852e+00+0.00000000e+00j  4.97922846e+00+0.00000000e+00j
  4.52705760e+00+0.00000000e+00j  5.52499045e+00+0.00000000e+00j
  3.71034539e+00+0.00000000e+00j  2.52982074e+00+0.00000000e+00j
  1.64930914e+00+0.00000000e+00j  3.09863111e+00+0.00000000e+00j
  2.47580958e-15+0.00000000e+00j -1.65288848e-15+0.00000000e+00j
  5.70081743e-16+1.20046461e-15j  5.70081743e-16-1.20046461e-15j
  1.33129042e-15+4.80421842e-16j  1.33129042e-15-4.80421842e-16j
  1.44519840e-15+0.00000000e+00j  1.40636211e-15+0.00000000e+00j
 -1.47858887e-15+8.84405337e-17j -1.47858887e-15-8.84405337e-17j
 -1.47610964e-15+0.00000000e+00j -1.33943776e-15+3.03287179e-16j
 -1.33943776e-15-3.03287179e-16j  1.17479495e-15+2.21117339e-17j
  1.17479495e-15-2.21117339e-17j -1.15151539e-15+8.26742487e-17j
 -1.15151539e-15-8.26742487e-17j -1.12802567e-15+0.00000000e+00j
 -1.08451833e-15+0.00000000e+00j  1.02197629e-15+2.85596995e-16j
  1.02197629e-15-2.85596995e-16j  1.02268194e-15+0.00000000e+00j
  9.89138793e-16+6.13869191e-17j  9.89138793e-16-6.13869191e-17j
 -9.80675487e-16+3.95311889e-17j -9.80675487e-16-3.95311889e-17j
 -6.98934425e-16+4.62379327e-16j -6.98934425e-16-4.62379327e-16j
  6.52409059e-17+7.09044637e-16j  6.52409059e-17-7.09044637e-16j
 -7.44399213e-16+3.34330406e-16j -7.44399213e-16-3.34330406e-16j
  6.04362753e-16+4.45088804e-16j  6.04362753e-16-4.45088804e-16j
  8.39313147e-16+0.00000000e+00j  7.77349757e-16+1.90177405e-17j
  7.77349757e-16-1.90177405e-17j -7.81922370e-16+4.84393310e-17j
 -7.81922370e-16-4.84393310e-17j -3.65466314e-16+5.25458894e-16j
 -3.65466314e-16-5.25458894e-16j  6.67557910e-16+2.40530111e-16j
  6.67557910e-16-2.40530111e-16j -6.90801669e-16+1.90538043e-16j
 -6.90801669e-16-1.90538043e-16j  6.89579968e-16+0.00000000e+00j
 -6.90425746e-16+8.22982436e-17j -6.90425746e-16-8.22982436e-17j
  5.84160501e-16+1.62731053e-16j  5.84160501e-16-1.62731053e-16j
  5.92981668e-16+6.50535753e-17j  5.92981668e-16-6.50535753e-17j
 -5.67859413e-16+2.10055555e-16j -5.67859413e-16-2.10055555e-16j
 -6.37057598e-16+0.00000000e+00j  4.39735544e-16+2.62183104e-16j
  4.39735544e-16-2.62183104e-16j -5.57996374e-16+0.00000000e+00j
  4.38732849e-16+2.32957100e-16j  4.38732849e-16-2.32957100e-16j
 -4.98811569e-16+1.88570905e-16j -4.98811569e-16-1.88570905e-16j
 -7.62149586e-17+3.94355055e-16j -7.62149586e-17-3.94355055e-16j
  9.88091240e-17+3.76443403e-16j  9.88091240e-17-3.76443403e-16j
  2.19555183e-16+3.26239636e-16j  2.19555183e-16-3.26239636e-16j
 -4.04160113e-16+2.28606742e-16j -4.04160113e-16-2.28606742e-16j
  4.43182710e-16+0.00000000e+00j -5.03655195e-16+4.06385692e-17j
 -5.03655195e-16-4.06385692e-17j  4.26953284e-16+1.00903760e-16j
  4.26953284e-16-1.00903760e-16j -1.33926664e-16+2.59310667e-16j
 -1.33926664e-16-2.59310667e-16j  2.31675633e-16+2.27615670e-16j
  2.31675633e-16-2.27615670e-16j -3.64022954e-16+1.17142945e-16j
 -3.64022954e-16-1.17142945e-16j  3.61809868e-16+1.00643295e-16j
  3.61809868e-16-1.00643295e-16j -2.97742905e-16+1.58398709e-16j
 -2.97742905e-16-1.58398709e-16j -3.69130280e-16+5.85995989e-17j
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  1.16237986e-16-2.25550972e-16j -1.26423005e-17+2.02639856e-16j
 -1.26423005e-17-2.02639856e-16j  3.12944063e-16+0.00000000e+00j
 -2.55543703e-16+1.35768914e-16j -2.55543703e-16-1.35768914e-16j
 -3.07479316e-16+0.00000000e+00j  2.33658396e-16+1.03418260e-16j
  2.33658396e-16-1.03418260e-16j  3.00322137e-16+0.00000000e+00j
 -1.70117825e-17+1.55617544e-16j -1.70117825e-17-1.55617544e-16j
  1.18175179e-16+9.81707997e-17j  1.18175179e-16-9.81707997e-17j
  1.93400054e-16+0.00000000e+00j  2.52266634e-16+0.00000000e+00j
 -2.82466192e-17+5.94471069e-17j -2.82466192e-17-5.94471069e-17j
  7.82410154e-17+0.00000000e+00j  1.37243778e-16+0.00000000e+00j
 -1.56616772e-16+4.90419987e-17j -1.56616772e-16-4.90419987e-17j
 -2.22041433e-16+0.00000000e+00j -8.73831456e-17+0.00000000e+00j
 -2.47697797e-32+0.00000000e+00j]
In [98]:
# Run PCA and fit the model
myPCA = PCA()
x = myPCA.fit(chile_data_s)
In [99]:
# Plotting the varaince explained by each component
plt.bar(range(1,len(x.explained_variance_ )+1),x.explained_variance_ratio_)
plt.ylabel('Explained variance')
plt.xlabel('Components')
plt.title('All Principle Components')
Out[99]:
Text(0.5, 1.0, 'All Principle Components')
In [100]:
# Deciding on the number of principal componenets to chose
plt.plot(range(1, len(x.explained_variance_)+1), x.explained_variance_ratio_.cumsum())
plt.ylabel('Explained variance')
plt.xlabel('Components')
pass
In [101]:
# Calculate the numeric values of principal components
x.explained_variance_ratio_.cumsum()
Out[101]:
array([0.3312722 , 0.46639855, 0.55741953, 0.64277411, 0.71110055,
       0.76175181, 0.80878144, 0.84938504, 0.8859778 , 0.91924751,
       0.94651514, 0.96928723, 0.98787909, 1.        , 1.        ])

will use only first 7 components, which explain 80.9 % of variance. instead of 127 columns in our dataset we have 7 uncorrelated components, which make the analysis easier.

In [102]:
# Calculate loadings
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s)

# Print
print("The loadings are are \n {}".format(pca_model.components_))
The loadings are are 
 [[-1.02217277e-02  1.41037360e-01  1.42111012e-02 -1.37097650e-02
   1.48126933e-01  1.41714050e-01  2.62006734e-02  1.45785498e-01
   1.42282465e-01  1.43981514e-01  1.49518705e-01 -6.39023470e-02
   1.42446079e-01 -6.39178387e-03  5.96387885e-04  5.32322647e-02
  -1.63127131e-05 -3.30567184e-02  2.43490034e-02  5.21125334e-02
   3.18376106e-02  1.05020380e-01  1.48514167e-01  7.63980384e-02
  -5.32750221e-02 -7.06357362e-02  1.47735023e-01  2.71380925e-04
   1.28537731e-01 -1.85715489e-02  1.13235572e-02  1.62682207e-02
  -5.14656908e-02 -5.55592351e-02 -4.38927341e-02 -4.87076457e-02
  -1.45880074e-02 -4.25539300e-02 -3.84577670e-02  3.47270343e-02
  -2.96110996e-02 -2.15771150e-02 -1.96119212e-02 -8.84224563e-03
  -2.71928802e-02  2.39140242e-03 -4.22270005e-02  6.26971115e-03
   4.86259746e-02 -2.99982637e-03 -1.15861076e-02 -1.11659579e-02
   6.43580249e-02 -9.89538209e-02  1.40895726e-01  1.49377598e-01
   1.48704776e-01 -6.45389930e-02  1.47072963e-01  1.15175790e-01
  -9.28076189e-02  1.16292747e-01 -6.34460110e-02  9.57160535e-02
   2.77316430e-02  6.52916011e-02  1.47341008e-01  1.11729312e-01
   4.44651515e-02  1.12103095e-01  1.39411096e-01  8.15840454e-02
  -1.31390988e-03 -9.63317592e-02  1.31666069e-01  1.50513770e-01
  -1.00911201e-03 -5.60410948e-03  1.21231940e-01  4.47844192e-02
  -6.19518778e-02 -8.31992133e-02 -5.39516607e-02  1.18082096e-01
  -9.26207288e-03  3.72414462e-02  2.71188256e-03 -9.30911277e-02
   1.47610093e-01  3.94378418e-02 -6.45389930e-02 -5.39361158e-03
  -1.51012060e-02  6.85642046e-02  1.49296323e-01  1.37780814e-01
   1.90113337e-02  3.23552136e-02 -3.02192455e-02 -5.01482406e-02
   3.23122380e-02  1.38453321e-01 -2.75134702e-02  1.50327402e-01
   1.49832735e-01  1.50551386e-01  1.31343718e-01  1.50786519e-01
   7.38468273e-02  1.30646124e-02 -1.47740135e-02  1.44507354e-01
  -2.05268518e-02  1.31076361e-01  1.17723336e-01  1.44948459e-01
   4.89221440e-02 -8.58789890e-02  9.97237194e-02  1.35835987e-01
  -3.85195938e-02 -4.62745837e-02  1.43469832e-01  1.05095446e-01
  -1.87113188e-02  1.22927421e-01 -2.28174352e-02]
 [-1.42850674e-01 -2.40126881e-02 -3.06496826e-02  4.69590494e-02
   3.99439896e-02  2.93094979e-02  1.50160518e-01  3.12141822e-02
   5.27602633e-02 -2.79721559e-02  2.90993768e-02  6.21690115e-02
   5.87494750e-02 -1.58800224e-01 -1.20886239e-01 -3.08733529e-02
  -1.42830585e-01 -5.32258020e-02  6.35729060e-02 -1.20581281e-01
   1.12267792e-02  4.65750902e-02 -1.94946190e-03 -2.23805998e-02
   1.54846657e-02  1.63609471e-01  3.60597141e-02  1.09019337e-01
   2.00563407e-03 -2.31405146e-02 -1.20857022e-01 -5.06358765e-02
   7.41212419e-02  3.78887948e-02 -5.87333556e-02  4.43269301e-02
  -9.63676632e-02  5.73854903e-02  1.11618917e-01 -3.61032889e-02
   3.48077205e-02  4.60613957e-03 -2.62170737e-02 -1.12417338e-01
   3.10272591e-02 -4.79931997e-04  3.67430593e-02  6.32192976e-02
   1.08043371e-02  2.16665682e-01 -3.08210184e-02  4.92948107e-02
   1.37572582e-01  1.55755846e-01  9.24558718e-03  3.76115043e-02
   4.50413853e-02  1.78047704e-01  2.17743961e-02  6.18007977e-02
   1.77965505e-01 -2.31185563e-02  1.82390887e-01  8.43134761e-02
  -7.97165119e-02 -2.83040638e-02  5.07720896e-02  1.01563930e-02
  -3.03780747e-02 -7.92376081e-03  8.74431750e-02  7.82469213e-02
   3.57178263e-02  1.38720304e-01 -8.55059584e-02  2.99635760e-02
   5.36668948e-02  4.83548054e-02  1.17544708e-01 -1.12832859e-01
   1.42433653e-01  1.39674340e-01  1.14641953e-01  5.34717470e-02
   1.10442449e-01  1.88122008e-01 -1.36423150e-01  1.70280868e-01
   2.98889538e-02  3.14542128e-02  1.78047704e-01 -1.14767557e-01
   2.61158893e-03  1.94059395e-01 -1.59033508e-02  6.32650475e-02
   8.17478194e-02 -1.59042834e-03  3.77322125e-02  1.05221569e-01
   4.33848591e-02 -5.27496319e-03 -8.07295613e-02  3.01055216e-02
   2.25135464e-02  2.06186915e-02  8.15982183e-02  8.31942882e-03
  -8.54377404e-02 -2.03834536e-01  5.71555460e-02  5.76981684e-02
  -3.39297870e-02  6.66457627e-02  4.05385558e-02  5.98324747e-02
   4.96324530e-02  1.85308543e-01  3.77021420e-02  2.83747936e-02
  -3.13711714e-02  1.95302458e-01 -2.82318875e-02 -3.26396836e-02
  -9.44428331e-02 -5.44532834e-02 -1.02404618e-01]
 [-5.78435895e-02  4.96810272e-02 -9.91065127e-04 -1.42975648e-01
   1.12693412e-02 -1.12670409e-02 -8.54963080e-02  1.99599747e-02
   1.09894900e-02  5.99054095e-02  1.78505824e-02  1.72576717e-01
   1.51145904e-02  5.70769185e-03  7.03764794e-02 -8.06014608e-02
   2.23243437e-02 -4.56666893e-02 -7.85271483e-02  1.35412923e-01
  -3.20091627e-02  1.02355311e-02  4.29521889e-02 -5.77667119e-03
   2.08401145e-01  1.28658929e-01  1.15272838e-02 -9.17001288e-02
   2.99572681e-02 -1.21886143e-01 -4.36643011e-02 -9.06321623e-02
   1.81059031e-01  1.83863095e-01  1.96852213e-01  9.98128767e-02
   1.41996553e-01 -2.72212856e-02  1.47098076e-01  2.40527086e-03
  -2.02330935e-01  1.48491604e-01 -4.57792864e-02  8.20557921e-02
   1.97205903e-01  7.98946413e-02  1.63478377e-01 -3.89582625e-02
   1.66868637e-01 -4.21279177e-02  1.59477554e-01 -1.30721098e-02
  -1.56881127e-01 -4.66099885e-03  4.49485656e-02  1.71227317e-02
   1.88305793e-02  7.82787871e-02  2.61744914e-02 -1.34530458e-01
   6.78399585e-02  9.64811812e-02 -2.06638531e-02 -5.04074202e-02
  -4.44773544e-02  1.92588811e-01  1.07602349e-02  3.51242764e-04
  -3.22503650e-02 -3.78812977e-02  3.02086844e-02  2.55196881e-02
   8.28798856e-02 -5.85449713e-02  3.90233669e-02  9.91165359e-03
   1.61814802e-01  1.26714010e-01  1.36042487e-02  1.35194891e-01
   9.38903281e-02  5.08497326e-02  1.12777229e-02  9.86490757e-02
   8.86736446e-02 -7.83466688e-02  9.82094149e-02  7.24783016e-02
  -1.05012770e-02  9.22795142e-02  7.82787871e-02  1.39811054e-01
  -3.00197926e-02 -3.21612213e-02  2.99540887e-02  3.93424750e-02
  -7.53873168e-02 -1.09608018e-01  5.95715830e-02  4.88104865e-02
  -1.57707590e-01  3.86083607e-02 -7.71422930e-02  9.92929856e-03
   3.71387512e-02  1.31582850e-02  2.11920628e-02  2.72783227e-02
  -6.40712498e-02  1.95900073e-02 -1.09607929e-01 -4.99273195e-03
  -3.93833060e-02 -6.69970590e-02  1.09478661e-01  1.51982205e-02
  -1.20179954e-01  7.02819367e-02 -9.74501219e-02  5.58247091e-02
   6.10585686e-02  8.09301972e-02  5.61377859e-02  1.17654325e-01
   7.91418061e-02  6.68449008e-02  3.71733500e-03]
 [ 5.32209894e-02  4.19671245e-02  2.25844394e-01  1.24067654e-01
   1.26991156e-02 -3.40094830e-02  1.33144748e-01 -6.00483181e-02
  -6.72074568e-02 -9.41645823e-03 -2.26873744e-02 -6.94777580e-02
  -6.62311051e-02  3.21694718e-02  3.69704854e-02  1.70903616e-01
  -3.46771760e-02  2.14018237e-01  5.69729495e-02 -2.60391809e-03
   2.52132837e-01  1.68915751e-01 -3.35514342e-02  2.37185252e-01
  -8.30786555e-02 -5.53838721e-03 -1.55554645e-03 -4.93774799e-02
   9.83757421e-02 -5.77367465e-02  2.20148696e-01  2.60633168e-01
   6.02186582e-02  9.69350199e-02 -8.44482917e-02 -1.10525531e-01
  -9.96013993e-02  1.79043814e-01 -1.63400194e-02  2.32615692e-01
  -4.67233181e-02  2.61234700e-02 -1.14884901e-01  6.79577142e-02
   6.03093281e-03  2.35525822e-01  1.71507596e-01  2.58598463e-02
  -3.65356846e-04  1.91837170e-03  6.66316306e-03  4.46380246e-02
   4.64211282e-02 -7.85041313e-04  7.30656588e-02 -6.92776120e-03
  -1.86801461e-02  1.46374043e-02 -1.79358331e-02 -4.45381386e-02
   2.57075722e-02  1.43793833e-01  3.05686718e-02 -8.82033237e-02
   5.10626302e-02  6.49356270e-02 -2.32963289e-02  1.89729559e-01
   1.46679684e-01  7.12565787e-02 -3.27414854e-02  6.56059473e-03
   1.94111094e-02 -9.77421307e-02  1.32320990e-02 -2.71560687e-02
   5.94387801e-02  2.09057218e-01  2.56642528e-02 -4.77011050e-02
  -3.52093567e-02  1.20809482e-01  1.58419069e-01 -5.43763283e-02
  -1.56250527e-02  5.47677150e-02  2.68948191e-02  7.77043227e-03
   6.20934638e-02  4.78442018e-02  1.46374043e-02 -9.05388015e-02
  -2.10136603e-02  2.34455837e-02 -1.84083583e-02 -4.72928620e-02
  -6.46500981e-02 -3.43990874e-02  5.77625565e-02  4.50374578e-03
  -7.36591175e-03 -6.62017158e-02 -2.95587866e-02 -3.99642449e-02
  -8.08177622e-03 -2.08355628e-02 -6.66006994e-02 -2.76718272e-02
  -6.91783376e-03 -4.34687553e-02 -1.07995998e-02 -5.80999315e-02
  -1.43630285e-02 -6.41921678e-02 -3.91490797e-02 -1.43760979e-02
   1.29861529e-02 -1.02921800e-02 -8.57765271e-02 -1.22467358e-04
   4.03484149e-03 -4.30400325e-03 -1.79423992e-02  1.09171507e-02
   4.12714059e-02 -3.11398116e-02 -5.15360746e-02]
 [-1.74594284e-01  2.56163423e-02  5.40773555e-02 -3.04416042e-02
  -3.56920263e-02  6.43862649e-02  3.24871809e-02 -3.63703498e-02
  -3.94801359e-02 -4.94153043e-02 -3.68298556e-02 -5.25711247e-02
  -1.81806543e-02 -1.52787450e-01 -1.43293748e-01  1.76106151e-02
  -1.85846900e-01  8.48250402e-02  7.28855309e-02 -8.88603708e-02
  -5.05881854e-02  2.44592459e-02  1.30096969e-02 -4.57260361e-02
   9.07634466e-02 -8.92673044e-02 -6.65270158e-02  1.13274748e-04
  -1.15213478e-01  2.27082766e-02  7.62376369e-02 -4.82985990e-02
  -1.10828161e-01 -9.17029949e-02 -6.51326949e-02  7.33462886e-03
  -7.64600770e-03  1.11470999e-01 -1.44253968e-01 -7.31901811e-02
   8.70459151e-02  2.58589111e-01  7.82393974e-02 -1.63984349e-01
   1.73911524e-01  1.43634504e-01 -6.40327476e-02  1.06741379e-01
   2.05454273e-01 -3.34007492e-04 -1.86599839e-01 -1.64559622e-01
  -3.17771747e-02 -3.45883156e-02  8.36382528e-03 -2.39324165e-02
  -2.74284862e-02 -7.84857844e-02 -3.38348441e-02  1.61650907e-02
   3.08974136e-02  7.75113488e-02  5.95501350e-03  6.52242803e-02
  -9.35076645e-02  1.20375478e-01 -2.71126279e-02 -3.05403849e-02
   4.41145959e-02  1.40205457e-01 -2.14866206e-02 -1.25444742e-01
   1.98972143e-01  5.65799452e-02 -4.40843593e-03 -3.32928240e-02
   1.56302811e-01  4.99818698e-02 -3.26982347e-02  1.62972108e-01
   5.10556238e-03 -6.92489405e-03 -7.34085144e-02  2.12864653e-02
  -5.79059648e-02  4.67480594e-02 -8.04448196e-02 -5.10877103e-02
  -2.37314244e-02 -5.71386802e-02 -7.84857844e-02  1.92466496e-02
   1.13505973e-01 -2.35442368e-02 -3.82342125e-02 -4.26347946e-02
   1.43283910e-01  5.86442023e-02  1.29589430e-01 -1.66944052e-01
  -3.12839659e-02 -1.74981631e-02 -4.37993361e-02 -2.94786653e-02
  -2.33244595e-02 -4.45165583e-02 -6.61555969e-02 -2.46801551e-02
  -1.65114953e-01  4.35374577e-02  5.22898804e-02 -3.08052636e-02
  -3.45745538e-02  1.82036457e-04  1.16356627e-01 -5.38268607e-02
   1.30297925e-01 -4.12756083e-02  4.65566160e-02  1.05088041e-01
   9.70321695e-02 -6.70730336e-02  2.47038683e-02  1.47171857e-01
   6.48610369e-02  8.13295993e-02  1.38813444e-01]
 [ 8.21640726e-02 -4.14165160e-02  1.18439304e-01  5.98417989e-02
   1.26971553e-02 -9.18262786e-02 -5.67326756e-02  6.94818607e-03
   3.06427494e-02 -3.84910856e-02 -3.48526509e-03  1.20190376e-01
  -1.45810136e-02 -4.40133503e-02 -2.93723479e-02 -7.16037457e-02
  -2.69330739e-02  9.35692210e-02 -2.38935311e-01  4.68067472e-02
   2.33922860e-02  8.97216909e-02 -5.24104933e-02 -2.57279319e-02
   9.22037851e-04  1.09107659e-02  2.57506062e-02  2.45096670e-01
  -2.37260833e-02 -8.04470319e-02 -8.96732435e-02 -1.00387787e-02
  -1.25585510e-02 -8.84401466e-02 -1.18430679e-01 -1.24557601e-01
   1.16382048e-01 -1.57736161e-01 -8.93218471e-02 -1.69022631e-02
  -5.86275776e-02  8.97984961e-02 -2.18969322e-01 -1.77153814e-02
   1.26078929e-01  1.30843333e-02  4.27792325e-02  7.82896261e-02
   1.19941655e-01 -1.21645271e-01  6.30162527e-02  4.18103408e-02
   7.01392786e-02  3.28719531e-02 -5.31711436e-02 -1.48324417e-02
  -9.56242949e-03 -6.68966196e-02  3.19784875e-02  8.42694158e-02
   4.28283412e-03 -3.61620141e-02  6.82464179e-03  1.49727976e-03
   7.20377795e-02  1.02707042e-01  2.97666442e-02 -1.32040141e-03
  -1.50382874e-02 -1.08548345e-01  2.51402617e-02  1.82852978e-01
   2.30448071e-01  1.58886311e-02 -7.80243107e-02  7.46179118e-04
  -8.84959262e-02  7.35620211e-02  9.02308231e-02 -8.69777725e-03
  -1.15373796e-01 -3.32011877e-02 -8.29809163e-02 -6.85624437e-02
  -2.43924532e-01 -6.96035135e-02 -8.46642040e-02 -2.34660619e-02
  -6.13102342e-03 -4.93991313e-02 -6.68966196e-02  1.11837894e-01
   1.17894821e-02 -3.51517358e-03 -2.88359238e-02  5.83934775e-02
  -8.80932345e-02  1.10496339e-01 -2.51194778e-02  1.31564547e-01
   1.90316996e-01 -1.09704555e-01 -1.45691644e-01 -5.51329375e-03
  -1.96290879e-02 -9.37416922e-03  5.34291204e-02 -1.36330710e-02
  -1.12673903e-01  3.52835910e-03  2.19898579e-01  7.45666785e-03
  -8.32290393e-02  6.09728625e-02  8.53090569e-02  1.86705407e-02
  -1.79462749e-01 -1.13656936e-02 -9.77483212e-02  3.61123972e-02
  -1.19871398e-01 -9.73195690e-02 -1.43094872e-02 -8.63322426e-02
  -8.59605530e-02 -1.19415018e-01 -4.51676836e-02]
 [-6.58069748e-02 -1.06615014e-02  5.72384318e-02  1.71061688e-01
   3.72238369e-02 -1.18432375e-02  4.62606926e-02 -4.23961502e-02
  -4.04486166e-02  1.76626722e-02  5.41718740e-03  1.61662732e-01
  -5.10107497e-02  1.75978413e-02  1.59774275e-01 -1.32413370e-01
   2.85868492e-02 -7.98662484e-02  1.60063510e-01  2.25673347e-01
   1.19660718e-01  2.62997367e-02 -2.11105357e-02  6.72354866e-03
  -6.08769330e-03 -2.12555098e-02 -3.73808793e-02  1.79388382e-01
   7.24531559e-02 -4.02512879e-02  9.57100207e-02  4.30499718e-02
   1.31207249e-01  1.02854958e-01  1.37081759e-01  1.31547579e-01
   1.85348908e-01 -3.31193252e-02 -7.81417334e-02  3.61058311e-02
   1.31456843e-01 -6.17327965e-02  4.90073944e-02  2.19894757e-01
  -6.74469828e-02 -2.86761694e-02 -2.80231945e-03  2.18734049e-02
   4.81462052e-03 -4.24459926e-03 -9.79460626e-02  6.60758908e-02
   8.90538784e-02 -1.85604969e-02  3.22959853e-03 -3.04215504e-02
  -3.27152566e-02 -2.44045960e-02 -6.63696254e-02  4.10317664e-02
  -5.63298828e-03  5.82692704e-02  1.35819914e-01  1.43613014e-01
   3.77825441e-03  1.07196981e-01 -2.19915119e-02 -1.26494904e-02
  -1.43313968e-01  1.27003156e-01 -1.05952126e-02  5.57103583e-03
   6.49267596e-02  4.05778616e-02 -5.97767913e-02 -3.51772607e-02
   1.43498538e-01  4.88003338e-02  6.45655752e-02  3.93489980e-02
   1.36734861e-01 -5.09735622e-02 -7.00759630e-02  8.12674889e-02
   8.17034942e-02  1.03190608e-01  2.51242839e-01 -4.31441219e-02
   3.35102179e-03 -1.14643910e-01 -2.44045960e-02  1.47300864e-01
   1.06032895e-01  8.08976520e-02 -3.60320414e-02 -2.12161727e-02
   2.47292721e-01  1.92550691e-01 -8.63944132e-02 -1.53080450e-02
   6.03223016e-02 -4.36902336e-03  2.42403921e-02 -1.62963731e-02
  -1.66910480e-02 -1.66684115e-02 -4.98311364e-02 -3.18855586e-02
   1.12399953e-01  1.08431187e-01  1.31300125e-01 -2.63390194e-02
   8.76691962e-02  1.85002638e-02 -8.58666355e-03 -1.94067766e-02
   1.01625952e-01 -3.12403812e-02  6.97000422e-02 -5.12448485e-02
  -1.06509616e-01 -6.31841360e-02 -4.70389766e-02 -1.42026131e-03
  -8.78981429e-02 -1.26806878e-02 -8.00146528e-02]]
In [103]:
# Explore the importance of each feature for principle components
pca = PCA(n_components = 7).fit(chile_data_s)
vars = pca.explained_variance_ratio_
c_names = chile_data_s.columns
sum = 0

print('Variance:  Projected dimension')
print('------------------------------')
for idx, row in enumerate(pca.components_):
    output = '{0:4.1f}%:    '.format(100.0 * vars[idx])
    output += " + ".join("{0:5.2f} * {1:s}".format(val, name) \
                      for val, name in zip(row, c_names))
    sum += 100*vars[idx]
    print(output)

print('Total variance explained by the 7 components {0:4.1f}%'.format(sum))
# Total variance explained by the 7 components 80.9%
Variance:  Projected dimension
------------------------------
33.1%:    -0.01 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.14 * NUMBER OF CULTURAL CENTERS +  0.01 * WORLD CULTURAL HERITAGE SITES + -0.01 * NUMBER OF ARCHEOLOGICAL SITES +  0.15 * NATIONAL MONUMENTS +  0.14 * MUSEUMS +  0.03 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.15 * THEATERS +  0.14 * NUMBER OF THEATER PLAYS PER YEAR +  0.14 * LIBRARIES +  0.15 * GALERIES + -0.06 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.14 * NUMBER OF EXHIBITS + -0.01 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.00 * MAJOR SPORTS EVENTS PER YEAR +  0.05 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.00 * ARTWORK SITES + -0.03 * POPULAR ARCHITECTURE SITES +  0.02 * HISTORICAL SITES +  0.05 * LOCAL MARKETS +  0.03 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.11 * CULTURA SITES LEVEL II (NATIONAL) +  0.15 * CULTURAL SITES LEVEL I (LOCAL) +  0.08 * HERITAGE ARCHITECTURAL HOUSES + -0.05 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.07 * NATIONAL PROTECTED SITES (%) +  0.15 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.13 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.01 * NUMBER OF BEACHES AND BEACH RESORTS +  0.02 * LAND AFFECTED BY WILDFIRES + -0.05 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.06 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.04 * RIVERS, LAKES AND WATERFALLS + -0.05 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.01 * GEISERS AND THERMAL CENTERS + -0.04 * PIERS AND SEASHORES + -0.04 * GLACIERS AND WINTER VACATION LOCATIONS +  0.03 * VALLEYS + -0.03 * DESERTS AND DUNES + -0.02 * ISLANDS AND PENINSULAS + -0.02 * PALEONTOLOGY SITES + -0.01 * HIKING TRAILS + -0.03 * PRESERVED SITES +  0.00 * SEASHORE PROTECTED SITES + -0.04 * BIOSHPERE RESERVES +  0.01 * % AVAILABLE WORKFORCE +  0.05 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.01 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION +  0.06 * AVERAGE NUMBER OF YEARS STUDYING + -0.10 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.14 * TOURISM-ORIENTED INSTITUTIONS +  0.15 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.15 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.06 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.15 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.12 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.09 * ROOMS PER 1000 HABITANTS +  0.12 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.06 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.10 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.03 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.07 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.15 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.11 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.04 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.11 * NATIONAL TOURISTS ARRIVALS +  0.14 * INTERNATIONAL TOURISTS ARRIVALS +  0.08 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY + -0.00 * DENSITY OF AIRPORTS + -0.10 * DENSITY OF ROADS AND HIGHWAYS +  0.13 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.15 * NUMBER OF VEHICLES + -0.00 * VISITORS TO PROTECTED SITES + -0.01 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.12 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.04 * SECONDARY ROADS (KMS) + -0.06 * NUMBER OF INTERNATIONAL BORDER GATES + -0.08 * Density of restaurants and other food services per 100,000 inhabitants + -0.05 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.12 * Car rental companies + -0.01 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.04 * Density of beds in hospitals per 10,000 inhabitants +  0.00 * Number of spas + -0.09 * Density of gambling casinos per million inhabitants +  0.15 * Number of golf courses +  0.04 * Number of craft centers + -0.06 * Density of tour guides per 100,000 inhabitants + -0.01 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.07 * Penetration of telephone lines in service per 100 inhabitants +  0.15 * Density of service stations +  0.14 * Number of tour-operator companies certified with the tourism quality seal +  0.02 * Perception of exposure to crime (%) +  0.03 * Percentage of victimized households with at least one victim + -0.03 * Density of homicides per million inhabitants + -0.05 * Density of crimes against public health per million inhabitants +  0.03 * Black figure index +  0.14 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime +  0.15 * Number of declared crimes +  0.15 * Number of crimes investigated +  0.15 * Number of accidents (roads, air and waterways) +  0.13 * Illegal commerce +  0.15 * Number of Carabineros +  0.07 * Unemployment rate +  0.01 * Poverty rate + -0.01 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.14 * Number of strikes carried out + -0.02 * Average (days) duration of a strike +  0.13 * Person-day cost of a strike +  0.12 * Density of Bank Branches per million inhabitants +  0.14 * Floating population +  0.05 * Volume of exports + -0.09 * Density of Tourist Information Offices per million inhabitants +  0.10 * Number of visits to Tourist Information Offices +  0.14 * Average monthly global searches by tourist attraction on the internet + -0.04 * National tourism promotion budget (Thousands of USD) + -0.05 * International tourism promotion budget (Thousands of USD) +  0.14 * Investments in public infrastructure made by the Ministry of Public Works +  0.11 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.02 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.12 * Funds obtained from FNRD (Thousands of pesos) + -0.02 * Number of regional strategic development plans
13.5%:    -0.14 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.02 * NUMBER OF CULTURAL CENTERS + -0.03 * WORLD CULTURAL HERITAGE SITES +  0.05 * NUMBER OF ARCHEOLOGICAL SITES +  0.04 * NATIONAL MONUMENTS +  0.03 * MUSEUMS +  0.15 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.03 * THEATERS +  0.05 * NUMBER OF THEATER PLAYS PER YEAR + -0.03 * LIBRARIES +  0.03 * GALERIES +  0.06 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.06 * NUMBER OF EXHIBITS + -0.16 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.12 * MAJOR SPORTS EVENTS PER YEAR + -0.03 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.14 * ARTWORK SITES + -0.05 * POPULAR ARCHITECTURE SITES +  0.06 * HISTORICAL SITES + -0.12 * LOCAL MARKETS +  0.01 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.05 * CULTURA SITES LEVEL II (NATIONAL) + -0.00 * CULTURAL SITES LEVEL I (LOCAL) + -0.02 * HERITAGE ARCHITECTURAL HOUSES +  0.02 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.16 * NATIONAL PROTECTED SITES (%) +  0.04 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.11 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.00 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.12 * NUMBER OF BEACHES AND BEACH RESORTS + -0.05 * LAND AFFECTED BY WILDFIRES +  0.07 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.04 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.06 * RIVERS, LAKES AND WATERFALLS +  0.04 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS +  0.06 * PIERS AND SEASHORES +  0.11 * GLACIERS AND WINTER VACATION LOCATIONS + -0.04 * VALLEYS +  0.03 * DESERTS AND DUNES +  0.00 * ISLANDS AND PENINSULAS + -0.03 * PALEONTOLOGY SITES + -0.11 * HIKING TRAILS +  0.03 * PRESERVED SITES + -0.00 * SEASHORE PROTECTED SITES +  0.04 * BIOSHPERE RESERVES +  0.06 * % AVAILABLE WORKFORCE +  0.01 * % POPULATION ORIENTED TOWARDS TOURISM +  0.22 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.03 * 5 POPULATION WITH PRIMARY EDUCATION +  0.05 * % POPULATION WITH SECONDARY EDUCATION +  0.14 * AVERAGE NUMBER OF YEARS STUDYING +  0.16 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.01 * TOURISM-ORIENTED INSTITUTIONS +  0.04 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.05 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.18 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.06 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.18 * ROOMS PER 1000 HABITANTS + -0.02 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.18 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.08 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.08 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND + -0.03 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.05 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.01 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.03 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.01 * NATIONAL TOURISTS ARRIVALS +  0.09 * INTERNATIONAL TOURISTS ARRIVALS +  0.08 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.04 * DENSITY OF AIRPORTS +  0.14 * DENSITY OF ROADS AND HIGHWAYS + -0.09 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.03 * NUMBER OF VEHICLES +  0.05 * VISITORS TO PROTECTED SITES +  0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.12 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.11 * SECONDARY ROADS (KMS) +  0.14 * NUMBER OF INTERNATIONAL BORDER GATES +  0.14 * Density of restaurants and other food services per 100,000 inhabitants +  0.11 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.05 * Car rental companies +  0.11 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.19 * Density of beds in hospitals per 10,000 inhabitants + -0.14 * Number of spas +  0.17 * Density of gambling casinos per million inhabitants +  0.03 * Number of golf courses +  0.03 * Number of craft centers +  0.18 * Density of tour guides per 100,000 inhabitants + -0.11 * Number of thermal centers +  0.00 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.19 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations +  0.06 * Number of tour-operator companies certified with the tourism quality seal +  0.08 * Perception of exposure to crime (%) + -0.00 * Percentage of victimized households with at least one victim +  0.04 * Density of homicides per million inhabitants +  0.11 * Density of crimes against public health per million inhabitants +  0.04 * Black figure index + -0.01 * Budget for public safety (Thousands of $) + -0.08 * Percentage of households that reported at least one crime +  0.03 * Number of declared crimes +  0.02 * Number of crimes investigated +  0.02 * Number of accidents (roads, air and waterways) +  0.08 * Illegal commerce +  0.01 * Number of Carabineros + -0.09 * Unemployment rate + -0.20 * Poverty rate +  0.06 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.06 * Number of strikes carried out + -0.03 * Average (days) duration of a strike +  0.07 * Person-day cost of a strike +  0.04 * Density of Bank Branches per million inhabitants +  0.06 * Floating population +  0.05 * Volume of exports +  0.19 * Density of Tourist Information Offices per million inhabitants +  0.04 * Number of visits to Tourist Information Offices +  0.03 * Average monthly global searches by tourist attraction on the internet + -0.03 * National tourism promotion budget (Thousands of USD) +  0.20 * International tourism promotion budget (Thousands of USD) + -0.03 * Investments in public infrastructure made by the Ministry of Public Works + -0.03 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.05 * Funds obtained from FNRD (Thousands of pesos) + -0.10 * Number of regional strategic development plans
 9.1%:    -0.06 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.05 * NUMBER OF CULTURAL CENTERS + -0.00 * WORLD CULTURAL HERITAGE SITES + -0.14 * NUMBER OF ARCHEOLOGICAL SITES +  0.01 * NATIONAL MONUMENTS + -0.01 * MUSEUMS + -0.09 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.02 * THEATERS +  0.01 * NUMBER OF THEATER PLAYS PER YEAR +  0.06 * LIBRARIES +  0.02 * GALERIES +  0.17 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP +  0.02 * NUMBER OF EXHIBITS +  0.01 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.07 * MAJOR SPORTS EVENTS PER YEAR + -0.08 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS +  0.02 * ARTWORK SITES + -0.05 * POPULAR ARCHITECTURE SITES + -0.08 * HISTORICAL SITES +  0.14 * LOCAL MARKETS + -0.03 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.01 * CULTURA SITES LEVEL II (NATIONAL) +  0.04 * CULTURAL SITES LEVEL I (LOCAL) + -0.01 * HERITAGE ARCHITECTURAL HOUSES +  0.21 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.13 * NATIONAL PROTECTED SITES (%) +  0.01 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.09 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.03 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.12 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.04 * NUMBER OF BEACHES AND BEACH RESORTS + -0.09 * LAND AFFECTED BY WILDFIRES +  0.18 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.18 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) +  0.20 * RIVERS, LAKES AND WATERFALLS +  0.10 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.14 * GEISERS AND THERMAL CENTERS + -0.03 * PIERS AND SEASHORES +  0.15 * GLACIERS AND WINTER VACATION LOCATIONS +  0.00 * VALLEYS + -0.20 * DESERTS AND DUNES +  0.15 * ISLANDS AND PENINSULAS + -0.05 * PALEONTOLOGY SITES +  0.08 * HIKING TRAILS +  0.20 * PRESERVED SITES +  0.08 * SEASHORE PROTECTED SITES +  0.16 * BIOSHPERE RESERVES + -0.04 * % AVAILABLE WORKFORCE +  0.17 * % POPULATION ORIENTED TOWARDS TOURISM + -0.04 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.16 * 5 POPULATION WITH PRIMARY EDUCATION + -0.01 * % POPULATION WITH SECONDARY EDUCATION + -0.16 * AVERAGE NUMBER OF YEARS STUDYING + -0.00 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.04 * TOURISM-ORIENTED INSTITUTIONS +  0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS +  0.02 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.13 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.07 * ROOMS PER 1000 HABITANTS +  0.10 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. + -0.02 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.05 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.04 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.19 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.01 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.00 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.03 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.04 * NATIONAL TOURISTS ARRIVALS +  0.03 * INTERNATIONAL TOURISTS ARRIVALS +  0.03 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.08 * DENSITY OF AIRPORTS + -0.06 * DENSITY OF ROADS AND HIGHWAYS +  0.04 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.01 * NUMBER OF VEHICLES +  0.16 * VISITORS TO PROTECTED SITES +  0.13 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.01 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.14 * SECONDARY ROADS (KMS) +  0.09 * NUMBER OF INTERNATIONAL BORDER GATES +  0.05 * Density of restaurants and other food services per 100,000 inhabitants +  0.01 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.10 * Car rental companies +  0.09 * Densidad de camas en hospitales por cada 10.000 habitantes + -0.08 * Density of beds in hospitals per 10,000 inhabitants +  0.10 * Number of spas +  0.07 * Density of gambling casinos per million inhabitants + -0.01 * Number of golf courses +  0.09 * Number of craft centers +  0.08 * Density of tour guides per 100,000 inhabitants +  0.14 * Number of thermal centers + -0.03 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.03 * Penetration of telephone lines in service per 100 inhabitants +  0.03 * Density of service stations +  0.04 * Number of tour-operator companies certified with the tourism quality seal + -0.08 * Perception of exposure to crime (%) + -0.11 * Percentage of victimized households with at least one victim +  0.06 * Density of homicides per million inhabitants +  0.05 * Density of crimes against public health per million inhabitants + -0.16 * Black figure index +  0.04 * Budget for public safety (Thousands of $) + -0.08 * Percentage of households that reported at least one crime +  0.01 * Number of declared crimes +  0.04 * Number of crimes investigated +  0.01 * Number of accidents (roads, air and waterways) +  0.02 * Illegal commerce +  0.03 * Number of Carabineros + -0.06 * Unemployment rate +  0.02 * Poverty rate + -0.11 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.00 * Number of strikes carried out + -0.04 * Average (days) duration of a strike + -0.07 * Person-day cost of a strike +  0.11 * Density of Bank Branches per million inhabitants +  0.02 * Floating population + -0.12 * Volume of exports +  0.07 * Density of Tourist Information Offices per million inhabitants + -0.10 * Number of visits to Tourist Information Offices +  0.06 * Average monthly global searches by tourist attraction on the internet +  0.06 * National tourism promotion budget (Thousands of USD) +  0.08 * International tourism promotion budget (Thousands of USD) +  0.06 * Investments in public infrastructure made by the Ministry of Public Works +  0.12 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.08 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.07 * Funds obtained from FNRD (Thousands of pesos) +  0.00 * Number of regional strategic development plans
 8.5%:     0.05 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.04 * NUMBER OF CULTURAL CENTERS +  0.23 * WORLD CULTURAL HERITAGE SITES +  0.12 * NUMBER OF ARCHEOLOGICAL SITES +  0.01 * NATIONAL MONUMENTS + -0.03 * MUSEUMS +  0.13 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.06 * THEATERS + -0.07 * NUMBER OF THEATER PLAYS PER YEAR + -0.01 * LIBRARIES + -0.02 * GALERIES + -0.07 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.07 * NUMBER OF EXHIBITS +  0.03 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.04 * MAJOR SPORTS EVENTS PER YEAR +  0.17 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.03 * ARTWORK SITES +  0.21 * POPULAR ARCHITECTURE SITES +  0.06 * HISTORICAL SITES + -0.00 * LOCAL MARKETS +  0.25 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.17 * CULTURA SITES LEVEL II (NATIONAL) + -0.03 * CULTURAL SITES LEVEL I (LOCAL) +  0.24 * HERITAGE ARCHITECTURAL HOUSES + -0.08 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.01 * NATIONAL PROTECTED SITES (%) + -0.00 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS + -0.05 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.10 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.06 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.22 * NUMBER OF BEACHES AND BEACH RESORTS +  0.26 * LAND AFFECTED BY WILDFIRES +  0.06 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.10 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.08 * RIVERS, LAKES AND WATERFALLS + -0.11 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.10 * GEISERS AND THERMAL CENTERS +  0.18 * PIERS AND SEASHORES + -0.02 * GLACIERS AND WINTER VACATION LOCATIONS +  0.23 * VALLEYS + -0.05 * DESERTS AND DUNES +  0.03 * ISLANDS AND PENINSULAS + -0.11 * PALEONTOLOGY SITES +  0.07 * HIKING TRAILS +  0.01 * PRESERVED SITES +  0.24 * SEASHORE PROTECTED SITES +  0.17 * BIOSHPERE RESERVES +  0.03 * % AVAILABLE WORKFORCE + -0.00 * % POPULATION ORIENTED TOWARDS TOURISM +  0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.01 * 5 POPULATION WITH PRIMARY EDUCATION +  0.04 * % POPULATION WITH SECONDARY EDUCATION +  0.05 * AVERAGE NUMBER OF YEARS STUDYING + -0.00 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.07 * TOURISM-ORIENTED INSTITUTIONS + -0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.02 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS +  0.01 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.02 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS + -0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.03 * ROOMS PER 1000 HABITANTS +  0.14 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.03 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) + -0.09 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.05 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.06 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS +  0.19 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.15 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.07 * NATIONAL TOURISTS ARRIVALS + -0.03 * INTERNATIONAL TOURISTS ARRIVALS +  0.01 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.02 * DENSITY OF AIRPORTS + -0.10 * DENSITY OF ROADS AND HIGHWAYS +  0.01 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES +  0.06 * VISITORS TO PROTECTED SITES +  0.21 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.05 * SECONDARY ROADS (KMS) + -0.04 * NUMBER OF INTERNATIONAL BORDER GATES +  0.12 * Density of restaurants and other food services per 100,000 inhabitants +  0.16 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.05 * Car rental companies + -0.02 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.05 * Density of beds in hospitals per 10,000 inhabitants +  0.03 * Number of spas +  0.01 * Density of gambling casinos per million inhabitants +  0.06 * Number of golf courses +  0.05 * Number of craft centers +  0.01 * Density of tour guides per 100,000 inhabitants + -0.09 * Number of thermal centers + -0.02 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.02 * Penetration of telephone lines in service per 100 inhabitants + -0.02 * Density of service stations + -0.05 * Number of tour-operator companies certified with the tourism quality seal + -0.06 * Perception of exposure to crime (%) + -0.03 * Percentage of victimized households with at least one victim +  0.06 * Density of homicides per million inhabitants +  0.00 * Density of crimes against public health per million inhabitants + -0.01 * Black figure index + -0.07 * Budget for public safety (Thousands of $) + -0.03 * Percentage of households that reported at least one crime + -0.04 * Number of declared crimes + -0.01 * Number of crimes investigated + -0.02 * Number of accidents (roads, air and waterways) + -0.07 * Illegal commerce + -0.03 * Number of Carabineros + -0.01 * Unemployment rate + -0.04 * Poverty rate + -0.01 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.06 * Number of strikes carried out + -0.01 * Average (days) duration of a strike + -0.06 * Person-day cost of a strike + -0.04 * Density of Bank Branches per million inhabitants + -0.01 * Floating population +  0.01 * Volume of exports + -0.01 * Density of Tourist Information Offices per million inhabitants + -0.09 * Number of visits to Tourist Information Offices + -0.00 * Average monthly global searches by tourist attraction on the internet +  0.00 * National tourism promotion budget (Thousands of USD) + -0.00 * International tourism promotion budget (Thousands of USD) + -0.02 * Investments in public infrastructure made by the Ministry of Public Works +  0.01 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.04 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.03 * Funds obtained from FNRD (Thousands of pesos) + -0.05 * Number of regional strategic development plans
 6.8%:    -0.17 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR +  0.03 * NUMBER OF CULTURAL CENTERS +  0.05 * WORLD CULTURAL HERITAGE SITES + -0.03 * NUMBER OF ARCHEOLOGICAL SITES + -0.04 * NATIONAL MONUMENTS +  0.06 * MUSEUMS +  0.03 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.04 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR + -0.05 * LIBRARIES + -0.04 * GALERIES + -0.05 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.02 * NUMBER OF EXHIBITS + -0.15 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.14 * MAJOR SPORTS EVENTS PER YEAR +  0.02 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.19 * ARTWORK SITES +  0.08 * POPULAR ARCHITECTURE SITES +  0.07 * HISTORICAL SITES + -0.09 * LOCAL MARKETS + -0.05 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.02 * CULTURA SITES LEVEL II (NATIONAL) +  0.01 * CULTURAL SITES LEVEL I (LOCAL) + -0.05 * HERITAGE ARCHITECTURAL HOUSES +  0.09 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.09 * NATIONAL PROTECTED SITES (%) + -0.07 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.00 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.12 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED +  0.02 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.08 * NUMBER OF BEACHES AND BEACH RESORTS + -0.05 * LAND AFFECTED BY WILDFIRES + -0.11 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.07 * RIVERS, LAKES AND WATERFALLS +  0.01 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS + -0.01 * GEISERS AND THERMAL CENTERS +  0.11 * PIERS AND SEASHORES + -0.14 * GLACIERS AND WINTER VACATION LOCATIONS + -0.07 * VALLEYS +  0.09 * DESERTS AND DUNES +  0.26 * ISLANDS AND PENINSULAS +  0.08 * PALEONTOLOGY SITES + -0.16 * HIKING TRAILS +  0.17 * PRESERVED SITES +  0.14 * SEASHORE PROTECTED SITES + -0.06 * BIOSHPERE RESERVES +  0.11 * % AVAILABLE WORKFORCE +  0.21 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.19 * 5 POPULATION WITH PRIMARY EDUCATION + -0.16 * % POPULATION WITH SECONDARY EDUCATION + -0.03 * AVERAGE NUMBER OF YEARS STUDYING + -0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.01 * TOURISM-ORIENTED INSTITUTIONS + -0.02 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.08 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.02 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.03 * ROOMS PER 1000 HABITANTS +  0.08 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.01 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.07 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR + -0.09 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.12 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.03 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS +  0.04 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.14 * NATIONAL TOURISTS ARRIVALS + -0.02 * INTERNATIONAL TOURISTS ARRIVALS + -0.13 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.20 * DENSITY OF AIRPORTS +  0.06 * DENSITY OF ROADS AND HIGHWAYS + -0.00 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.03 * NUMBER OF VEHICLES +  0.16 * VISITORS TO PROTECTED SITES +  0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR + -0.03 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.16 * SECONDARY ROADS (KMS) +  0.01 * NUMBER OF INTERNATIONAL BORDER GATES + -0.01 * Density of restaurants and other food services per 100,000 inhabitants + -0.07 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.02 * Car rental companies + -0.06 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.05 * Density of beds in hospitals per 10,000 inhabitants + -0.08 * Number of spas + -0.05 * Density of gambling casinos per million inhabitants + -0.02 * Number of golf courses + -0.06 * Number of craft centers + -0.08 * Density of tour guides per 100,000 inhabitants +  0.02 * Number of thermal centers +  0.11 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.02 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.04 * Number of tour-operator companies certified with the tourism quality seal +  0.14 * Perception of exposure to crime (%) +  0.06 * Percentage of victimized households with at least one victim +  0.13 * Density of homicides per million inhabitants + -0.17 * Density of crimes against public health per million inhabitants + -0.03 * Black figure index + -0.02 * Budget for public safety (Thousands of $) + -0.04 * Percentage of households that reported at least one crime + -0.03 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.04 * Number of accidents (roads, air and waterways) + -0.07 * Illegal commerce + -0.02 * Number of Carabineros + -0.17 * Unemployment rate +  0.04 * Poverty rate +  0.05 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.03 * Number of strikes carried out + -0.03 * Average (days) duration of a strike +  0.00 * Person-day cost of a strike +  0.12 * Density of Bank Branches per million inhabitants + -0.05 * Floating population +  0.13 * Volume of exports + -0.04 * Density of Tourist Information Offices per million inhabitants +  0.05 * Number of visits to Tourist Information Offices +  0.11 * Average monthly global searches by tourist attraction on the internet +  0.10 * National tourism promotion budget (Thousands of USD) + -0.07 * International tourism promotion budget (Thousands of USD) +  0.02 * Investments in public infrastructure made by the Ministry of Public Works +  0.15 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) +  0.06 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population +  0.08 * Funds obtained from FNRD (Thousands of pesos) +  0.14 * Number of regional strategic development plans
 5.1%:     0.08 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.04 * NUMBER OF CULTURAL CENTERS +  0.12 * WORLD CULTURAL HERITAGE SITES +  0.06 * NUMBER OF ARCHEOLOGICAL SITES +  0.01 * NATIONAL MONUMENTS + -0.09 * MUSEUMS + -0.06 * % OF POPULATION THAT ATTENDS MUSEUMS +  0.01 * THEATERS +  0.03 * NUMBER OF THEATER PLAYS PER YEAR + -0.04 * LIBRARIES + -0.00 * GALERIES +  0.12 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.01 * NUMBER OF EXHIBITS + -0.04 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR + -0.03 * MAJOR SPORTS EVENTS PER YEAR + -0.07 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS + -0.03 * ARTWORK SITES +  0.09 * POPULAR ARCHITECTURE SITES + -0.24 * HISTORICAL SITES +  0.05 * LOCAL MARKETS +  0.02 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.09 * CULTURA SITES LEVEL II (NATIONAL) + -0.05 * CULTURAL SITES LEVEL I (LOCAL) + -0.03 * HERITAGE ARCHITECTURAL HOUSES +  0.00 * % OF LAND THAT CORRESPONDS TO FORESTS +  0.01 * NATIONAL PROTECTED SITES (%) +  0.03 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.25 * TOXIC WASTE DISPOSAL (TONS/100 hab.) + -0.02 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.08 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS + -0.09 * NUMBER OF BEACHES AND BEACH RESORTS + -0.01 * LAND AFFECTED BY WILDFIRES + -0.01 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) + -0.09 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) + -0.12 * RIVERS, LAKES AND WATERFALLS + -0.12 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.12 * GEISERS AND THERMAL CENTERS + -0.16 * PIERS AND SEASHORES + -0.09 * GLACIERS AND WINTER VACATION LOCATIONS + -0.02 * VALLEYS + -0.06 * DESERTS AND DUNES +  0.09 * ISLANDS AND PENINSULAS + -0.22 * PALEONTOLOGY SITES + -0.02 * HIKING TRAILS +  0.13 * PRESERVED SITES +  0.01 * SEASHORE PROTECTED SITES +  0.04 * BIOSHPERE RESERVES +  0.08 * % AVAILABLE WORKFORCE +  0.12 * % POPULATION ORIENTED TOWARDS TOURISM + -0.12 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) +  0.06 * 5 POPULATION WITH PRIMARY EDUCATION +  0.04 * % POPULATION WITH SECONDARY EDUCATION +  0.07 * AVERAGE NUMBER OF YEARS STUDYING +  0.03 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS + -0.05 * TOURISM-ORIENTED INSTITUTIONS + -0.01 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.01 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.07 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) +  0.03 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.08 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR +  0.00 * ROOMS PER 1000 HABITANTS + -0.04 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.01 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.00 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.07 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.10 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION +  0.03 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.00 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.02 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) + -0.11 * NATIONAL TOURISTS ARRIVALS +  0.03 * INTERNATIONAL TOURISTS ARRIVALS +  0.18 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.23 * DENSITY OF AIRPORTS +  0.02 * DENSITY OF ROADS AND HIGHWAYS + -0.08 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) +  0.00 * NUMBER OF VEHICLES + -0.09 * VISITORS TO PROTECTED SITES +  0.07 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.09 * TOURIST'S ARRIVALS THROUGH BORDER LINES + -0.01 * SECONDARY ROADS (KMS) + -0.12 * NUMBER OF INTERNATIONAL BORDER GATES + -0.03 * Density of restaurants and other food services per 100,000 inhabitants + -0.08 * Density of People employed in restaurants and the like per 10,000 inhabitants + -0.07 * Car rental companies + -0.24 * Densidad de camas en hospitales por cada 10.000 habitantes + -0.07 * Density of beds in hospitals per 10,000 inhabitants + -0.08 * Number of spas + -0.02 * Density of gambling casinos per million inhabitants + -0.01 * Number of golf courses + -0.05 * Number of craft centers + -0.07 * Density of tour guides per 100,000 inhabitants +  0.11 * Number of thermal centers +  0.01 * Density of Sports Facilities and Venues per 10,000 inhabitants + -0.00 * Penetration of telephone lines in service per 100 inhabitants + -0.03 * Density of service stations +  0.06 * Number of tour-operator companies certified with the tourism quality seal + -0.09 * Perception of exposure to crime (%) +  0.11 * Percentage of victimized households with at least one victim + -0.03 * Density of homicides per million inhabitants +  0.13 * Density of crimes against public health per million inhabitants +  0.19 * Black figure index + -0.11 * Budget for public safety (Thousands of $) + -0.15 * Percentage of households that reported at least one crime + -0.01 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.01 * Number of accidents (roads, air and waterways) +  0.05 * Illegal commerce + -0.01 * Number of Carabineros + -0.11 * Unemployment rate +  0.00 * Poverty rate +  0.22 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants +  0.01 * Number of strikes carried out + -0.08 * Average (days) duration of a strike +  0.06 * Person-day cost of a strike +  0.09 * Density of Bank Branches per million inhabitants +  0.02 * Floating population + -0.18 * Volume of exports + -0.01 * Density of Tourist Information Offices per million inhabitants + -0.10 * Number of visits to Tourist Information Offices +  0.04 * Average monthly global searches by tourist attraction on the internet + -0.12 * National tourism promotion budget (Thousands of USD) + -0.10 * International tourism promotion budget (Thousands of USD) + -0.01 * Investments in public infrastructure made by the Ministry of Public Works + -0.09 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.12 * Funds obtained from FNRD (Thousands of pesos) + -0.05 * Number of regional strategic development plans
 4.7%:    -0.07 * CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR + -0.01 * NUMBER OF CULTURAL CENTERS +  0.06 * WORLD CULTURAL HERITAGE SITES +  0.17 * NUMBER OF ARCHEOLOGICAL SITES +  0.04 * NATIONAL MONUMENTS + -0.01 * MUSEUMS +  0.05 * % OF POPULATION THAT ATTENDS MUSEUMS + -0.04 * THEATERS + -0.04 * NUMBER OF THEATER PLAYS PER YEAR +  0.02 * LIBRARIES +  0.01 * GALERIES +  0.16 * % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP + -0.05 * NUMBER OF EXHIBITS +  0.02 * ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR +  0.16 * MAJOR SPORTS EVENTS PER YEAR + -0.13 * OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS +  0.03 * ARTWORK SITES + -0.08 * POPULAR ARCHITECTURE SITES +  0.16 * HISTORICAL SITES +  0.23 * LOCAL MARKETS +  0.12 * CULTURAL SITES LEVEL III (INTERNATIONAL) +  0.03 * CULTURA SITES LEVEL II (NATIONAL) + -0.02 * CULTURAL SITES LEVEL I (LOCAL) +  0.01 * HERITAGE ARCHITECTURAL HOUSES + -0.01 * % OF LAND THAT CORRESPONDS TO FORESTS + -0.02 * NATIONAL PROTECTED SITES (%) + -0.04 * % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS +  0.18 * TOXIC WASTE DISPOSAL (TONS/100 hab.) +  0.07 * NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED + -0.04 * ENVIRONMENTAL ISSUES PER MILLION HABITANTS +  0.10 * NUMBER OF BEACHES AND BEACH RESORTS +  0.04 * LAND AFFECTED BY WILDFIRES +  0.13 * NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) +  0.10 * NATURAL PROTECTED SITES LEVEL II (NATIONAL) +  0.14 * RIVERS, LAKES AND WATERFALLS +  0.13 * MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS +  0.19 * GEISERS AND THERMAL CENTERS + -0.03 * PIERS AND SEASHORES + -0.08 * GLACIERS AND WINTER VACATION LOCATIONS +  0.04 * VALLEYS +  0.13 * DESERTS AND DUNES + -0.06 * ISLANDS AND PENINSULAS +  0.05 * PALEONTOLOGY SITES +  0.22 * HIKING TRAILS + -0.07 * PRESERVED SITES + -0.03 * SEASHORE PROTECTED SITES + -0.00 * BIOSHPERE RESERVES +  0.02 * % AVAILABLE WORKFORCE +  0.00 * % POPULATION ORIENTED TOWARDS TOURISM + -0.00 * AVERAGE MONTHLY INCOME (CHILEAN PESOS) + -0.10 * 5 POPULATION WITH PRIMARY EDUCATION +  0.07 * % POPULATION WITH SECONDARY EDUCATION +  0.09 * AVERAGE NUMBER OF YEARS STUDYING + -0.02 * HIGHER EDUCATION AND TECHNICAL INSTITUTIONS +  0.00 * TOURISM-ORIENTED INSTITUTIONS + -0.03 * NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS + -0.03 * AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS + -0.02 * DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) + -0.07 * CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS +  0.04 * % OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR + -0.01 * ROOMS PER 1000 HABITANTS +  0.06 * NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. +  0.14 * TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) +  0.14 * AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR +  0.00 * AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND +  0.11 * NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION + -0.02 * NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS + -0.01 * TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS + -0.14 * TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) +  0.13 * NATIONAL TOURISTS ARRIVALS + -0.01 * INTERNATIONAL TOURISTS ARRIVALS +  0.01 * NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY +  0.06 * DENSITY OF AIRPORTS +  0.04 * DENSITY OF ROADS AND HIGHWAYS + -0.06 * % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) + -0.04 * NUMBER OF VEHICLES +  0.14 * VISITORS TO PROTECTED SITES +  0.05 * NUMBER OF CRUISES THAT ARRIVE PER YEAR +  0.06 * TOURIST'S ARRIVALS THROUGH BORDER LINES +  0.04 * SECONDARY ROADS (KMS) +  0.14 * NUMBER OF INTERNATIONAL BORDER GATES + -0.05 * Density of restaurants and other food services per 100,000 inhabitants + -0.07 * Density of People employed in restaurants and the like per 10,000 inhabitants +  0.08 * Car rental companies +  0.08 * Densidad de camas en hospitales por cada 10.000 habitantes +  0.10 * Density of beds in hospitals per 10,000 inhabitants +  0.25 * Number of spas + -0.04 * Density of gambling casinos per million inhabitants +  0.00 * Number of golf courses + -0.11 * Number of craft centers + -0.02 * Density of tour guides per 100,000 inhabitants +  0.15 * Number of thermal centers +  0.11 * Density of Sports Facilities and Venues per 10,000 inhabitants +  0.08 * Penetration of telephone lines in service per 100 inhabitants + -0.04 * Density of service stations + -0.02 * Number of tour-operator companies certified with the tourism quality seal +  0.25 * Perception of exposure to crime (%) +  0.19 * Percentage of victimized households with at least one victim + -0.09 * Density of homicides per million inhabitants + -0.02 * Density of crimes against public health per million inhabitants +  0.06 * Black figure index + -0.00 * Budget for public safety (Thousands of $) +  0.02 * Percentage of households that reported at least one crime + -0.02 * Number of declared crimes + -0.02 * Number of crimes investigated + -0.02 * Number of accidents (roads, air and waterways) + -0.05 * Illegal commerce + -0.03 * Number of Carabineros +  0.11 * Unemployment rate +  0.11 * Poverty rate +  0.13 * Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants + -0.03 * Number of strikes carried out +  0.09 * Average (days) duration of a strike +  0.02 * Person-day cost of a strike + -0.01 * Density of Bank Branches per million inhabitants + -0.02 * Floating population +  0.10 * Volume of exports + -0.03 * Density of Tourist Information Offices per million inhabitants +  0.07 * Number of visits to Tourist Information Offices + -0.05 * Average monthly global searches by tourist attraction on the internet + -0.11 * National tourism promotion budget (Thousands of USD) + -0.06 * International tourism promotion budget (Thousands of USD) + -0.05 * Investments in public infrastructure made by the Ministry of Public Works + -0.00 * Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) + -0.09 * Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population + -0.01 * Funds obtained from FNRD (Thousands of pesos) + -0.08 * Number of regional strategic development plans
Total variance explained by the 7 components 80.9%
In [104]:
# Calculate factor scores
pca_model = myPCA.fit_transform(chile_data_s)
PCcomponents = pd.DataFrame(data = pca_model, columns = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7'])
print("\n The Factor scores are")
PCcomponents
 The Factor scores are
Out[104]:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
0 -3.353498 1.608181 -1.582183 -0.237261 -3.449640 4.862151 0.772824
1 -2.164654 1.660241 -5.227525 -1.162747 2.753981 4.826127 2.701062
2 -0.727422 3.703057 -3.630520 -0.381449 3.291641 -4.412543 2.910755
3 -3.479076 0.821718 -4.731121 -1.918471 1.215268 -1.226846 0.481672
4 -0.830247 -3.134555 -2.097779 3.468263 1.943877 -0.477330 -3.890678
5 4.588837 -1.055439 -0.086863 10.589977 -1.359019 -0.558769 1.995606
6 21.826574 4.335464 0.555220 -2.699925 -1.391621 0.903598 -0.918454
7 -1.280227 -4.059490 -1.926913 0.722814 -3.125349 0.505362 -3.254364
8 -1.093260 -5.499579 -0.538222 -1.896991 -2.065633 -0.431173 -0.772332
9 3.139942 -3.838975 -0.460303 -2.765644 2.092178 -3.496457 -1.312946
10 -0.565803 -6.063523 4.890623 -2.249734 -2.930731 -0.981432 5.700374
11 -2.827197 -2.137913 1.260431 -2.341001 0.224917 0.360441 -1.389120
12 -0.385161 -0.194387 7.225696 1.156990 7.286542 2.600561 -0.380118
13 -6.999638 4.791132 3.947399 -1.193484 -1.698133 0.113016 -2.335758
14 -5.849170 9.064066 2.402060 0.908662 -2.788278 -2.586707 -0.308524
In [105]:
# Example of different variables in each component

# Fit the model
myPCA = PCA(n_components = 7)
pca_model = myPCA.fit(chile_data_s)
y_axis = [0,0,0,0,0,0,0]
for i in range(0,7):
    y_axis[i]=[np.mean(pca_model.components_[i][0:24]), np.mean(pca_model.components_[i][24:47]), 
               np.mean(pca_model.components_[i][47:59]), np.mean(pca_model.components_[i][59:69]),
               np.mean(pca_model.components_[i][69:81]), np.mean(pca_model.components_[i][81:96]),
               np.mean(pca_model.components_[i][96:108]), np.mean(pca_model.components_[i][108:117]),
               np.mean(pca_model.components_[i][117:122]), np.mean(pca_model.components_[i][122:127])]
# Plot
x_axis = ['CULTURAL HERITAGE', 'NATURAL RESOURCES', 'WORKFORCE DEVELOPMENT', 'TOURISM INFRASTRUCTURE', 'TOURISM MOBILITY',
          'TOURISM-RELATED SERVICES', 'SECURITY AND SAFETY ', 'ECONOMIC PERFORMANCE', 'TOURISM PROMOTION', 
          'GOVERNMENTAL INVOLVEMENT AND EFFICIENCY']
plt.plot(x_axis,y_axis[0], color = 'mediumaquamarine', label = "C1")
plt.plot(x_axis,y_axis[1], color = 'yellow', label = "C2")
plt.plot(x_axis,y_axis[2], color = 'pink', label = "C3")
plt.plot(x_axis,y_axis[3], color = 'steelblue', label = "C4")
plt.plot(x_axis,y_axis[4], color = 'salmon', label = "C5")
plt.plot(x_axis,y_axis[5], color = 'red', label = "C6")
plt.plot(x_axis,y_axis[6], color = 'orange', label = "C7")
plt.xticks(rotation = 90)
plt.title('Example of variable contributions to each principal component')
plt.legend()
pass

5. Developing a scoring system for 10 dimensions

Step 1 - Calculate a weighted average for each variable in principal components.

Multiply the percentage value of the explained variance by the percentage value of a feature in the selected principal component. As a result, a weighted average will be a new column in the dataframe with principal components.

In [106]:
# Creating a dataframe of weights
weights = pd.DataFrame(np.column_stack((chile_data_s.columns, pca_model.components_[0] * 
                                        pca_model.explained_variance_ratio_[0],
                                        pca_model.components_[1] * pca_model.explained_variance_ratio_[1],
                                        pca_model.components_[2] * pca_model.explained_variance_ratio_[2],
                                        pca_model.components_[3] * pca_model.explained_variance_ratio_[3],
                                        pca_model.components_[4] * pca_model.explained_variance_ratio_[4],
                                        pca_model.components_[5] * pca_model.explained_variance_ratio_[5],
                                        pca_model.components_[6] * pca_model.explained_variance_ratio_[6])))
weights = weights.set_index(0)

# Create a weighted average
weights['weighted_average'] = weights.sum(axis = 1)/np.sum(pca_model.explained_variance_ratio_)

# Print
weights.head()
Out[106]:
1 2 3 4 5 6 7 weighted_average
0
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR -0.00338617 -0.0193029 -0.00526498 0.00454266 -0.0119294 0.00416171 -0.00309488 -0.042377
NUMBER OF CULTURAL CENTERS 0.0467218 -0.00324475 0.00452202 0.00358209 0.00175027 -0.0020978 -0.000501406 0.062727
WORLD CULTURAL HERITAGE SITES 0.00470774 -0.00414158 -9.02077e-05 0.0192769 0.00369491 0.0059991 0.0026919 0.039737
NUMBER OF ARCHEOLOGICAL SITES -0.00454166 0.0063454 -0.0130138 0.0105897 -0.00207997 0.00303106 0.00804497 0.010356
NATIONAL MONUMENTS 0.0490703 0.00539749 0.00102575 0.00108393 -0.00243871 0.000643127 0.00175062 0.069898
In [107]:
# Ranking for dimension 1: CULTURAL HERITAGE AND EVENTS

# Create a dataframe for relevant variables
dim1 = chile_data_s.iloc[:, 0:24].mul(weights['weighted_average'][0:24], axis = 1)

# Create a score ranking
dim1['Ranking 1'] = dim1.sum(axis = 1)

# Sort by score
dim1.sort_values(by = 'Ranking 1', ascending = False).head()
Out[107]:
CULTURAL EVENTS SCHEDULED THROUGHOUT THE YEAR NUMBER OF CULTURAL CENTERS WORLD CULTURAL HERITAGE SITES NUMBER OF ARCHEOLOGICAL SITES NATIONAL MONUMENTS MUSEUMS % OF POPULATION THAT ATTENDS MUSEUMS THEATERS NUMBER OF THEATER PLAYS PER YEAR LIBRARIES GALERIES % OF POPULATION ASSOCIATED TO AN INDIGENOUS GROUP NUMBER OF EXHIBITS ARTISTIC EVENTS (MUSIC, DANCE AND FOLKLOR, THEATRE, ETC) PER YEAR MAJOR SPORTS EVENTS PER YEAR OBSERVATORIES, ZOOS, AQUARIUMS, BOTANICAL GARDENS ARTWORK SITES POPULAR ARCHITECTURE SITES HISTORICAL SITES LOCAL MARKETS CULTURAL SITES LEVEL III (INTERNATIONAL) CULTURA SITES LEVEL II (NATIONAL) CULTURAL SITES LEVEL I (LOCAL) HERITAGE ARCHITECTURAL HOUSES Ranking 1
Region
Metropolitana 0.026107 0.170834 -0.022214 -0.007255 0.243608 0.173958 0.022132 0.202428 0.213861 0.166276 0.218911 -0.007171 0.203508 0.026561 0.005815 0.011589 0.013684 -0.004720 -0.001878 0.017208 -0.003481 0.167385 0.190988 0.040105 2.068241
Valparaíso -0.009820 0.089552 0.111069 0.015921 0.058271 0.014342 0.050767 -0.009650 -0.011451 0.039243 0.034780 -0.007990 -0.005682 -0.021249 -0.008723 0.018832 0.013684 0.004562 0.020656 0.017208 0.153156 0.184191 0.017501 0.157885 0.927056
Biobío 0.022514 0.068162 -0.022214 -0.008263 -0.019525 0.069861 -0.010082 0.007181 -0.005453 0.051767 0.034780 -0.007353 0.048553 0.026561 0.005815 -0.010140 -0.002737 -0.002993 0.024412 -0.002647 -0.018399 -0.084701 0.058590 0.003398 0.227089
Los Lagos 0.036885 0.033939 0.044428 -0.008263 -0.015712 0.007402 -0.002028 -0.006284 -0.013258 -0.005487 -0.026597 0.008026 -0.013429 0.026561 0.005815 -0.010140 0.030106 0.002403 -0.013145 0.007280 -0.018399 0.073273 0.003805 -0.007429 0.139753
Antofagasta 0.036885 -0.030231 -0.022214 0.019951 -0.002746 0.042102 0.065979 -0.006284 -0.015751 -0.034114 -0.016367 -0.005715 -0.013429 0.010624 0.005815 0.011589 0.021895 -0.000619 0.050702 -0.022503 -0.003481 -0.014117 -0.005326 -0.015616 0.057032
In [108]:
# Ranking for dimension 2: NATURAL RESOURCES AND SUSTAINABILITY

# Create a dataframe for relevant variables
dim2 = chile_data_s.iloc[:, 24:47].mul(weights['weighted_average'][24:47], axis = 1)

# Create a score ranking
dim2['Ranking 2'] = dim2.sum(axis = 1)

# Sort the by score
dim2.sort_values(by = 'Ranking 2', ascending = False).head()
Out[108]:
% OF LAND THAT CORRESPONDS TO FORESTS NATIONAL PROTECTED SITES (%) % LAND THAT CORRESPONDS TO HUMAN SETTLEMENTS TOXIC WASTE DISPOSAL (TONS/100 hab.) NUMBER OF ENVIRONMENTAL COMPLAINTS PRESENTED ENVIRONMENTAL ISSUES PER MILLION HABITANTS NUMBER OF BEACHES AND BEACH RESORTS LAND AFFECTED BY WILDFIRES NATURAL PROTECTED SITES LEVEL III (INTERNATIONAL) NATURAL PROTECTED SITES LEVEL II (NATIONAL) RIVERS, LAKES AND WATERFALLS MOUNTAINS, VOLCANOES AND MOUNTAIN SYSTEMS GEISERS AND THERMAL CENTERS PIERS AND SEASHORES GLACIERS AND WINTER VACATION LOCATIONS VALLEYS DESERTS AND DUNES ISLANDS AND PENINSULAS PALEONTOLOGY SITES HIKING TRAILS PRESERVED SITES SEASHORE PROTECTED SITES BIOSHPERE RESERVES Ranking 2
Region
Valparaíso -0.002588 -0.002375 0.041800 -0.004914 0.120449 0.015085 0.025177 0.045138 0.009270 0.009542 0.014837 0.017461 -0.000444 0.005804 0.001470 0.067877 0.008466 -0.020484 0.029040 -0.012222 -1.530539e-02 0.099920 0.042806 0.495811
Metropolitana -0.002518 -0.002852 0.219560 0.015127 0.159164 0.015085 -0.010703 -0.009010 -0.012457 -0.008154 0.019230 0.008633 -0.000269 -0.007162 0.000134 0.004547 0.014239 -0.020484 0.029040 0.005726 -1.530539e-02 -0.028549 -0.021403 0.351619
Los Lagos 0.006125 0.001119 -0.024946 -0.013543 -0.034414 0.015085 0.000243 -0.009338 0.007098 0.001735 -0.005662 -0.011968 0.000607 0.002099 0.001470 -0.010068 0.014239 0.121681 0.029040 0.005726 1.173413e-01 0.099920 0.010701 0.324290
Coquimbo -0.003585 -0.002984 -0.019067 -0.035338 -0.034414 0.015085 0.011798 0.002968 -0.010284 -0.004771 0.008981 0.017461 -0.000794 0.012286 0.003474 0.048391 0.014239 0.020790 0.012906 0.005726 -1.530539e-02 0.057097 0.010701 0.115361
Arica y Parinacota 0.000000 0.002707 -0.015263 0.040780 -0.034414 0.015085 -0.007663 0.000000 0.009270 -0.005552 0.013373 -0.000196 0.000082 -0.005310 0.003474 -0.000325 0.002694 -0.020484 0.029040 0.003162 5.890698e-17 -0.028549 0.010701 0.012614
In [109]:
# Ranking for dimension 3: HUMAN RESOURCES AND TOURISM-RELATED WORKFORCE DEVELOPMENT

# Create a dataframe for relevant variables
dim3 = chile_data_s.iloc[:, 47:59].mul(weights['weighted_average'][47:59], axis = 1)

# Create a score ranking
dim3['Ranking 3'] = dim3.sum(axis = 1)

# Sort the dataframe by score
dim3.sort_values(by = 'Ranking 3', ascending = False).head()
Out[109]:
% AVAILABLE WORKFORCE % POPULATION ORIENTED TOWARDS TOURISM AVERAGE MONTHLY INCOME (CHILEAN PESOS) 5 POPULATION WITH PRIMARY EDUCATION % POPULATION WITH SECONDARY EDUCATION AVERAGE NUMBER OF YEARS STUDYING HIGHER EDUCATION AND TECHNICAL INSTITUTIONS TOURISM-ORIENTED INSTITUTIONS NUMBER OF COLLEGE STUDENTS IN TOURISM RELATED PROGRAMS AVERAGE NUMBER OF GRADUATES IN TOURISM-RELATED PROGRAMS DENSITY OF TOURISM GUIDES (PER 100.000 HABITANTS) CERTIFIED WORKERS ON HIGHLY-COMPETITIVE TOURISM STANDARDS Ranking 3
Region
Metropolitana 0.011089 0.068671 0.018237 0.000116 0.000051 0.073637 0.022265 0.191914 0.221282 0.226705 -0.000839 0.213654 1.046783
Valparaíso 0.000365 -0.021839 -0.009772 0.001271 -0.000280 0.037223 0.012478 0.110441 0.044006 0.032298 -0.000526 0.022925 0.228590
Los Lagos 0.004991 0.227734 -0.014812 0.001271 0.000469 -0.053812 0.007585 0.010863 -0.017382 -0.017898 -0.000156 0.006607 0.155459
Biobío -0.021759 -0.017555 -0.015082 0.002426 -0.000015 -0.005260 0.017372 0.074231 0.045474 0.033121 -0.000976 0.001031 0.113008
Tarapacá 0.068459 -0.013627 0.008067 0.008204 0.000469 0.043292 -0.005872 -0.034400 -0.029311 -0.028801 -0.000103 -0.031209 -0.014831
In [110]:
# Ranking for dimension 4: TOURISM INFRASTRUCTURE

# Create a dataframe for relevant variables
dim4 = chile_data_s.iloc[:, 59:69].mul(weights['weighted_average'][59:69], axis = 1)

# Create a score ranking
dim4['Ranking 4'] = dim4.sum(axis = 1)

# Sort the dataframe by score
dim4.sort_values(by = 'Ranking 4', ascending = False).head()
Out[110]:
% OF TOURISM-RELATED ROOMS AVAILABLE THROUGHOUT THE YEAR ROOMS PER 1000 HABITANTS NUMBER OF BEDS AVAILABLE IN HOTELS, HOSTELS, B&B, ETC. TOURISM-RELATED WORKFORCE (PER 10,000 EMPLOYEES) AVERAGE % OF OCCUPANCY THROUGHOUT THE YEAR AVERAGE NUMBER OF NIGHTS THAT TOURISTS SPEND NUMBER OF ESTABLISHMENTS WITH A TOURIST-RELATED CERTIFICATION NUMBER OF CERTIFIED CONSULTANTS FOR TOURISM-RELATED CERTIFICATIONS TOURISM-RELATED INVESTEMENTS (MILLION USD) BY CHAMBER OF COMMERCE MEMBERS TOURISM-RELATED INFRASTRUCTURE INVESTMENT (MILLION USD/YEAR) Ranking 4
Region
Metropolitana 0.132687 -0.005818 0.151380 -0.012278 0.130533 -0.002260 0.077979 0.241397 0.135150 0.014396 0.863165
Valparaíso -0.006067 -0.001894 0.156990 -0.001718 -0.030317 0.000969 0.067628 0.021063 0.172205 0.016512 0.395370
Los Lagos -0.026826 0.002688 0.099246 0.000632 -0.020164 0.004197 0.202194 -0.008037 -0.021318 -0.004548 0.228063
Coquimbo -0.017408 -0.000924 0.027021 -0.009892 -0.000926 -0.011945 -0.035884 -0.016352 0.052390 0.066190 0.052269
Araucanía -0.045862 -0.003406 0.038588 -0.006250 0.010830 -0.007103 0.077979 -0.020509 -0.028045 -0.006866 0.009356
In [111]:
# Ranking for dimension 5: TOURISM MOBILITY AND TRANSPORTATION INFRASTRUCTURE

# Create a dataframe for relevant variables
dim5 = chile_data_s.iloc[:, 69:81].mul(weights['weighted_average'][69:81], axis = 1)

# Create a score ranking
dim5['Ranking 5'] = dim5.sum(axis = 1)

# Sort the dataframe by score
dim5.sort_values(by = 'Ranking 5', ascending = False).head()
Out[111]:
NATIONAL TOURISTS ARRIVALS INTERNATIONAL TOURISTS ARRIVALS NUMBER OF PEOPLE TRAVELING OUT OF THE COUNTRY DENSITY OF AIRPORTS DENSITY OF ROADS AND HIGHWAYS % OF ROADS THAT ARE HIGHWAYS (FOUR LINES) NUMBER OF VEHICLES VISITORS TO PROTECTED SITES NUMBER OF CRUISES THAT ARRIVE PER YEAR TOURIST'S ARRIVALS THROUGH BORDER LINES SECONDARY ROADS (KMS) NUMBER OF INTERNATIONAL BORDER GATES Ranking 5
Region
Metropolitana 0.100554 0.259728 0.120251 -0.006189 0.030724 0.089124 0.215677 -0.013287 -0.033058 0.255464 0.006264 -0.005095 1.020156
Los Lagos 0.023358 0.004555 0.002669 0.163059 0.006485 -0.006800 -0.014619 0.128954 0.110080 -0.003165 0.053512 0.003396 0.471484
Valparaíso 0.092360 0.005276 0.010854 -0.017268 0.041864 0.027583 0.021942 0.008830 0.125416 0.062767 -0.011485 -0.005095 0.363045
Biobío 0.075951 -0.023888 -0.033787 -0.028659 0.024675 0.055932 0.021054 -0.025144 -0.033058 -0.055253 0.051458 -0.005095 0.024185
Arica y Parinacota -0.055501 -0.022816 0.128799 0.063833 0.001562 -0.034721 -0.031189 -0.037418 0.033399 -0.010150 -0.021722 0.003396 0.017473
In [112]:
# Ranking for dimension 6

# Create a dataframe for relevant variables
dim6 = chile_data_s.iloc[:, 81:96].mul(weights['weighted_average'][81:96], axis = 1)

# Create a score ranking
dim6['Ranking 6'] = dim6.sum(axis = 1)

# Sort by score
dim6.sort_values(by = 'Ranking 6', ascending = False).head()
Out[112]:
Density of restaurants and other food services per 100,000 inhabitants Density of People employed in restaurants and the like per 10,000 inhabitants Car rental companies Densidad de camas en hospitales por cada 10.000 habitantes Density of beds in hospitals per 10,000 inhabitants Number of spas Density of gambling casinos per million inhabitants Number of golf courses Number of craft centers Density of tour guides per 100,000 inhabitants Number of thermal centers Density of Sports Facilities and Venues per 10,000 inhabitants Penetration of telephone lines in service per 100 inhabitants Density of service stations Number of tour-operator companies certified with the tourism quality seal Ranking 6
Region
Metropolitana -0.002920 0.000430 0.192329 0.000379 0.065011 0.003205 0.010747 0.217912 0.022473 -0.000839 -0.000457 -0.002207 0.133232 0.173335 0.238759 1.051391
Valparaíso 0.002111 -0.000557 -0.001669 0.004191 0.022296 -0.004807 0.005638 0.102547 0.025638 -0.000526 -0.001028 -0.000191 0.033212 0.030820 -0.013057 0.204619
Antofagasta 0.000345 -0.000177 0.035879 0.007929 0.081541 0.000534 0.000127 -0.012818 0.012977 -0.000177 -0.001599 -0.002421 0.061526 -0.030009 -0.020052 0.133605
Magallanes y Antártica 0.002880 -0.000959 -0.026701 0.015925 0.092045 0.003205 -0.024034 -0.038455 -0.020258 0.004777 -0.002741 -0.000365 0.099192 -0.037830 -0.013057 0.053626
Los Lagos 0.000248 0.000088 0.048395 -0.004477 -0.015562 0.001424 -0.000233 -0.025637 -0.001266 -0.000156 0.001827 0.003351 -0.034224 -0.009588 0.007928 -0.027883
In [113]:
# Ranking for dimension 7

# Create a dataframe for relevant variables
dim7 = chile_data_s.iloc[:, 96:108].mul(weights['weighted_average'][96:108], axis = 1)

# Create a score ranking
dim7['Ranking 7'] = dim7.sum(axis = 1)

# Sort the by score
dim7.sort_values(by = 'Ranking 7', ascending = False).head()
Out[113]:
Perception of exposure to crime (%) Percentage of victimized households with at least one victim Density of homicides per million inhabitants Density of crimes against public health per million inhabitants Black figure index Budget for public safety (Thousands of $) Percentage of households that reported at least one crime Number of declared crimes Number of crimes investigated Number of accidents (roads, air and waterways) Illegal commerce Number of Carabineros Ranking 7
Region
Metropolitana 0.011710 0.008464 -0.005479 0.001525 0.013343 0.136506 0.033718 0.213969 0.218710 0.208089 0.213174 0.202417 1.256147
Biobío 0.007609 0.012892 -0.005879 0.002058 0.000601 0.064933 0.050537 0.027434 0.047618 0.020326 -0.023744 0.038650 0.243035
Valparaíso -0.009136 0.007283 0.000862 0.000485 0.003659 0.001270 0.008490 0.015183 0.047388 0.031594 -0.019705 0.027224 0.114596
Tarapacá 0.028456 0.059541 0.000922 0.002058 0.025066 -0.033773 0.064953 -0.019599 -0.038989 -0.029033 -0.006338 -0.030142 0.023122
Araucanía 0.014786 0.006397 -0.001826 0.001130 -0.012397 0.019406 -0.020343 -0.005805 -0.004950 -0.007010 -0.014791 -0.004805 -0.030207
In [114]:
# Ranking for dimension 8

# Create a dataframe for relevant variables
dim8 = chile_data_s.iloc[:, 108:117].mul(weights['weighted_average'][108:117], axis = 1)

# Create a score ranking
dim8['Ranking 8'] = dim8.sum(axis = 1)

# Sort the dataframe by score
dim8.sort_values(by = 'Ranking 8', ascending = False).head()
Out[114]:
Unemployment rate Poverty rate Density of crimes against property law and industrial privileges, and against intellectual property per million inhabitants Number of strikes carried out Average (days) duration of a strike Person-day cost of a strike Density of Bank Branches per million inhabitants Floating population Volume of exports Ranking 8
Region
Metropolitana -0.007245 0.014005 -0.002871 0.216196 0.008502 0.187929 0.226387 0.238420 0.016126 0.897449
Los Lagos 0.013886 -0.003121 -0.005038 -0.022959 0.021079 -0.030225 0.160958 -0.022756 -0.005356 0.106469
Antofagasta 0.000302 0.022568 -0.006617 0.003348 0.016399 0.011980 -0.016823 -0.018545 0.073141 0.085753
Tarapacá 0.007346 -0.012254 0.057963 -0.013393 0.000897 0.056389 -0.025542 -0.024169 0.000008 0.047245
Valparaíso -0.006239 -0.001979 -0.001363 -0.003826 0.013182 -0.008444 -0.024362 0.029428 0.005731 0.002128
In [115]:
# Ranking for dimension 9

# Create a dataframe for relevant variables
dim9 = chile_data_s.iloc[:, 117:122].mul(weights['weighted_average'][117:122], axis = 1)

# Create a score ranking
dim9['Ranking 9'] = dim9.sum(axis = 1)

# Sort the dataframe by score
dim9.sort_values(by = 'Ranking 9', ascending = False).head()
Out[115]:
Density of Tourist Information Offices per million inhabitants Number of visits to Tourist Information Offices Average monthly global searches by tourist attraction on the internet National tourism promotion budget (Thousands of USD) International tourism promotion budget (Thousands of USD) Ranking 9
Region
Metropolitana 0.003250 0.073905 0.234107 0.027813 -6.620432e-19 0.339075
Coquimbo 0.001995 0.017060 0.052264 -0.004692 -2.538132e-03 0.064088
Antofagasta -0.000266 0.052855 -0.002598 -0.007162 9.232840e-04 0.043753
Los Lagos -0.000396 -0.026438 0.090275 -0.022939 -1.446337e-03 0.039057
Araucanía 0.002475 0.006315 -0.024473 0.015141 -4.604072e-03 -0.005146
In [116]:
# Ranking for dimension 10

# Create a dataframe for relevant variables
dim10 = chile_data_s.iloc[:, 122:127].mul(weights['weighted_average'][122:127], axis = 1)

# Create a score ranking
dim10['Ranking 10'] = dim10.sum(axis = 1)

# Sort the dataframe by score
dim10.sort_values(by = 'Ranking 10', ascending = False).head()
Out[116]:
Investments in public infrastructure made by the Ministry of Public Works Investment Initiatives in projects or programs supported by government institutions (Thousands of Pesos) Contributions of government funds to the Tourism sector: CORFO, Sercotec, etc./population Funds obtained from FNRD (Thousands of pesos) Number of regional strategic development plans Ranking 10
Region
Metropolitana 0.170579 0.119339 0.020648 0.096992 0.025963 0.433521
Los Lagos 0.029156 0.102486 -0.007826 0.033758 -0.012981 0.144593
Biobío 0.069408 0.024954 -0.046092 0.090950 -0.051925 0.087295
Valparaíso 0.029846 0.000385 -0.010939 0.009322 0.025963 0.054576
Antofagasta -0.039602 0.030116 0.015149 0.007522 0.025963 0.039147
In [117]:
# Create an aggregated dataframe with all scores
final_scoring_data = pd.concat([dim1.iloc[:,-1:], dim2.iloc[:,-1:], dim3.iloc[:,-1:], dim4.iloc[:,-1:], dim5.iloc[:,-1:],
                                dim6.iloc[:,-1:], dim7.iloc[:,-1:], dim8.iloc[:,-1:], dim9.iloc[:,-1:], 
                                dim10.iloc[:,-1:]], axis = 1)

# Print
final_scoring_data
Out[117]:
Ranking 1 Ranking 2 Ranking 3 Ranking 4 Ranking 5 Ranking 6 Ranking 7 Ranking 8 Ranking 9 Ranking 10
Region
Arica y Parinacota -0.352040 0.012614 -0.200605 -0.133940 0.017473 -0.112601 -0.171233 -0.108058 -0.062982 -0.138605
Tarapacá -0.151878 -0.064461 -0.014831 -0.050027 -0.111674 -0.141030 0.023122 0.047245 -0.015176 -0.149593
Antofagasta 0.057032 -0.194991 -0.038854 -0.000971 -0.006765 0.133605 -0.074815 0.085753 0.043753 0.039147
Atacama -0.340943 -0.335391 -0.152789 -0.184250 -0.352432 -0.089080 -0.261799 -0.117217 -0.010370 -0.124519
Coquimbo -0.274780 0.115361 -0.103491 0.052269 -0.186785 -0.184503 -0.132779 -0.157317 0.064088 -0.017804
Valparaíso 0.927056 0.495811 0.228590 0.395370 0.363045 0.204619 0.114596 0.002128 -0.008493 0.054576
Metropolitana 2.068241 0.351619 1.046783 0.863165 1.020156 1.051391 1.256147 0.897449 0.339075 0.433521
O'Higgins -0.401523 -0.129822 -0.123471 -0.286507 -0.277080 -0.190150 -0.190980 -0.040408 -0.079938 -0.044923
Maule -0.466935 -0.108885 -0.217622 -0.224087 -0.204509 -0.259407 -0.173796 -0.144189 -0.062501 -0.011888
Biobío 0.227089 -0.277922 0.113008 -0.094783 0.024185 -0.043009 0.243035 -0.063800 -0.032632 0.087295
Araucanía -0.368866 -0.081632 -0.153601 0.009356 -0.046268 -0.096631 -0.030207 -0.173586 -0.005146 0.037159
Los Ríos -0.338965 -0.096590 -0.230853 -0.090791 -0.290327 -0.153838 -0.204846 -0.141987 -0.065986 -0.045419
Los Lagos 0.139753 0.324290 0.155459 0.228063 0.471484 -0.027883 -0.079900 0.106469 0.039057 0.144593
Aysén -0.483358 -0.009676 -0.197593 -0.363014 -0.400063 -0.145107 -0.134980 -0.066532 -0.046256 -0.173870
Magallanes y Antártica -0.239883 -0.000325 -0.110129 -0.119854 -0.020441 0.053626 -0.181565 -0.125951 -0.096493 -0.089669
In [118]:
# Create a list of column names
list_of_my_columns = [final_scoring_data['Ranking 1'], 
                      final_scoring_data['Ranking 2'], 
                      final_scoring_data['Ranking 3'],
                      final_scoring_data['Ranking 4'],
                      final_scoring_data['Ranking 5'],
                      final_scoring_data['Ranking 6'],
                      final_scoring_data['Ranking 7'],
                      final_scoring_data['Ranking 8'],
                      final_scoring_data['Ranking 9'],
                      final_scoring_data['Ranking 10']]

# Summarize and create an overall ranking
final_scoring_data['Overall Ranking'] = pd.concat(list_of_my_columns, axis = 1).sum(axis = 1)

# Print
final_scoring_data
Out[118]:
Ranking 1 Ranking 2 Ranking 3 Ranking 4 Ranking 5 Ranking 6 Ranking 7 Ranking 8 Ranking 9 Ranking 10 Overall Ranking
Region
Arica y Parinacota -0.352040 0.012614 -0.200605 -0.133940 0.017473 -0.112601 -0.171233 -0.108058 -0.062982 -0.138605 -1.249977
Tarapacá -0.151878 -0.064461 -0.014831 -0.050027 -0.111674 -0.141030 0.023122 0.047245 -0.015176 -0.149593 -0.628302
Antofagasta 0.057032 -0.194991 -0.038854 -0.000971 -0.006765 0.133605 -0.074815 0.085753 0.043753 0.039147 0.042893
Atacama -0.340943 -0.335391 -0.152789 -0.184250 -0.352432 -0.089080 -0.261799 -0.117217 -0.010370 -0.124519 -1.968790
Coquimbo -0.274780 0.115361 -0.103491 0.052269 -0.186785 -0.184503 -0.132779 -0.157317 0.064088 -0.017804 -0.825742
Valparaíso 0.927056 0.495811 0.228590 0.395370 0.363045 0.204619 0.114596 0.002128 -0.008493 0.054576 2.777297
Metropolitana 2.068241 0.351619 1.046783 0.863165 1.020156 1.051391 1.256147 0.897449 0.339075 0.433521 9.327548
O'Higgins -0.401523 -0.129822 -0.123471 -0.286507 -0.277080 -0.190150 -0.190980 -0.040408 -0.079938 -0.044923 -1.764803
Maule -0.466935 -0.108885 -0.217622 -0.224087 -0.204509 -0.259407 -0.173796 -0.144189 -0.062501 -0.011888 -1.873819
Biobío 0.227089 -0.277922 0.113008 -0.094783 0.024185 -0.043009 0.243035 -0.063800 -0.032632 0.087295 0.182466
Araucanía -0.368866 -0.081632 -0.153601 0.009356 -0.046268 -0.096631 -0.030207 -0.173586 -0.005146 0.037159 -0.909421
Los Ríos -0.338965 -0.096590 -0.230853 -0.090791 -0.290327 -0.153838 -0.204846 -0.141987 -0.065986 -0.045419 -1.659601
Los Lagos 0.139753 0.324290 0.155459 0.228063 0.471484 -0.027883 -0.079900 0.106469 0.039057 0.144593 1.501386
Aysén -0.483358 -0.009676 -0.197593 -0.363014 -0.400063 -0.145107 -0.134980 -0.066532 -0.046256 -0.173870 -2.020451
Magallanes y Antártica -0.239883 -0.000325 -0.110129 -0.119854 -0.020441 0.053626 -0.181565 -0.125951 -0.096493 -0.089669 -0.930684
In [119]:
final_scoring_data.style.highlight_null().render().split('\n')[:10]

def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

def highlight_max(s):
    '''
    highlight the maximum in a Series yellow.
    '''
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

final_scoring_data.style.\
    applymap(color_negative_red).\
    apply(highlight_max)
Out[119]:
Ranking 1 Ranking 2 Ranking 3 Ranking 4 Ranking 5 Ranking 6 Ranking 7 Ranking 8 Ranking 9 Ranking 10 Overall Ranking
Region
Arica y Parinacota -0.352040 0.012614 -0.200605 -0.133940 0.017473 -0.112601 -0.171233 -0.108058 -0.062982 -0.138605 -1.249977
Tarapacá -0.151878 -0.064461 -0.014831 -0.050027 -0.111674 -0.141030 0.023122 0.047245 -0.015176 -0.149593 -0.628302
Antofagasta 0.057032 -0.194991 -0.038854 -0.000971 -0.006765 0.133605 -0.074815 0.085753 0.043753 0.039147 0.042893
Atacama -0.340943 -0.335391 -0.152789 -0.184250 -0.352432 -0.089080 -0.261799 -0.117217 -0.010370 -0.124519 -1.968790
Coquimbo -0.274780 0.115361 -0.103491 0.052269 -0.186785 -0.184503 -0.132779 -0.157317 0.064088 -0.017804 -0.825742
Valparaíso 0.927056 0.495811 0.228590 0.395370 0.363045 0.204619 0.114596 0.002128 -0.008493 0.054576 2.777297
Metropolitana 2.068241 0.351619 1.046783 0.863165 1.020156 1.051391 1.256147 0.897449 0.339075 0.433521 9.327548
O'Higgins -0.401523 -0.129822 -0.123471 -0.286507 -0.277080 -0.190150 -0.190980 -0.040408 -0.079938 -0.044923 -1.764803
Maule -0.466935 -0.108885 -0.217622 -0.224087 -0.204509 -0.259407 -0.173796 -0.144189 -0.062501 -0.011888 -1.873819
Biobío 0.227089 -0.277922 0.113008 -0.094783 0.024185 -0.043009 0.243035 -0.063800 -0.032632 0.087295 0.182466
Araucanía -0.368866 -0.081632 -0.153601 0.009356 -0.046268 -0.096631 -0.030207 -0.173586 -0.005146 0.037159 -0.909421
Los Ríos -0.338965 -0.096590 -0.230853 -0.090791 -0.290327 -0.153838 -0.204846 -0.141987 -0.065986 -0.045419 -1.659601
Los Lagos 0.139753 0.324290 0.155459 0.228063 0.471484 -0.027883 -0.079900 0.106469 0.039057 0.144593 1.501386
Aysén -0.483358 -0.009676 -0.197593 -0.363014 -0.400063 -0.145107 -0.134980 -0.066532 -0.046256 -0.173870 -2.020451
Magallanes y Antártica -0.239883 -0.000325 -0.110129 -0.119854 -0.020441 0.053626 -0.181565 -0.125951 -0.096493 -0.089669 -0.930684